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  • 1.
    Abbas, Asad
    et al.
    Örebro University, Örebro University School of Business.
    Faiz, Ali
    Örebro University, Örebro University School of Business.
    Reasons for the failure of government IT projects in Pakistan: A Contemporary Study2013Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
  • 2.
    Abbas, Asad
    et al.
    Örebro University, Örebro University School of Business.
    Faiz, Ali
    Örebro University, Örebro University School of Business.
    Usefulness of Digital and Traditional Library in Higher Education2012Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
  • 3.
    Abbay, Kissery
    Örebro University, Swedish Business School at Örebro University.
    Gender Vis-à-vis Swedish Municipal Web sites2010Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
  • 4.
    Abdul Khaliq, Ali
    et al.
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Point-to-point safe navigation of a mobile robot using stigmergy and RFID technology2016In: 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2016, p. 1497-1504, article id 7759243Conference paper (Refereed)
    Abstract [en]

    Reliable autonomous navigation is still a challenging problem for robots with simple and inexpensive hardware. A key difficulty is the need to maintain an internal map of the environment and an accurate estimate of the robot’s position in this map. Recently, a stigmergic approach has been proposed in which a navigation map is stored into the environment, on a grid of RFID tags, and robots use it to optimally reach predefined goal points without the need for internal maps. While effective,this approach is limited to a predefined set of goal points. In this paper, we extend this approach to enable robots to travel to any point on the RFID floor, even if it was not previously identified as a goal location, as well as to keep a safe distance from any given critical location. Our approach produces safe, repeatable and quasi-optimal trajectories without the use of internal maps, self localization, or path planning. We report experiments run in a real apartment equipped with an RFID floor, in which a service robot either reaches or avoids a user who wears slippers equipped with an RFID tag reader.

  • 5.
    Abdulhomeed, Bashar
    Örebro University, Örebro University School of Business.
    Contemporary Research on e-democracy: A Literature Review2013Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
  • 6.
    Ackfjärd, Rickard
    et al.
    Örebro University, Örebro University School of Business.
    Karlsson, Mattias
    Örebro University, Örebro University School of Business.
    Förslag till ett nytt ramverk för utvärdering och utveckling av ARP-poisoning-skydd2016Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
  • 7.
    Adedoyin, Busayo Hannah
    Örebro University, Örebro University School of Business.
    Application of Integrated Behavioral Model (IBM) on Employees’ Information Security Behavioral responses to Phishing threats2021Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
  • 8.
    Adolfsson, Chandra
    et al.
    Örebro University, Swedish Business School at Örebro University.
    Håkansson, Alexandra
    Örebro University, Swedish Business School at Örebro University.
    En studie av sambandet mellan kvarstående bias och kostnad vid selektiv granskning i undersökningen Kortperiodisk Sysselsättningsstatistik: Analys av parameterval i verktyget Selekt2009Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
    Abstract [sv]

    Det har pågått ett intensivt utvecklingsarbete på Statistiska Centralbyrån (SCB) under de senaste åren i syfte att standardisera och effektivisera statistikproduktionsprocessen. I detta utvecklingsarbete har fokus främst riktats mot processerna insamling och granskning. Ett flertal studier har visat att det finns potential att reducera granskningens omfattning samtidigt som den övergripande kvaliteten i undersökningarna bibehålls. För att uppnå detta krävs att nya arbetssätt, metoder och verktyg utvecklas och implementeras.

    Den traditionella ansatsen på SCB har varit att i granskningsprocessen försöka hitta och rätta alla databearbetnings- och mätfel. Ingen skillnad har gjorts mellan stora och små fel eller om felen har någon effekt på statistiken eller inte. Detta är en ineffektiv ansats där stora resurser åtgår till att rätta fel som inte påverkar den statistiska redovisningen nämnvärt. I mer moderna ansatser betonas vikten av att hitta betydelsefulla fel som har stor påverkan på parameterskattningarna och att fel som inte ger någon påverkan bör lämnas som de är eller åtgärdas via imputering. Detta, att inte granska allt, kallas för selektiv granskning.

    SCB har beslutat att införa metoden selektiv granskning med poängfunktioner. Metoden fordrar att poängberäkningar görs, dessa utförs i verktyget Selekt. Verktyget ingår i den framtida verktygslådan för granskning som är under utveckling vid SCB och är uppbyggt av ett stort antal parametrar. För att uppnå så effektiv granskning som möjligt måste de mest lämpliga parametervärdena sökas för att sedan implementeras i Selekt.

    I denna studie har ett datamaterial från undersökningen Kortperiodisk Sysselsättningsstatistik, privat sektor (KSP) använts för att studera sambanden mellan statistikens kvalitet och valet av parametrar i Selekt.  Valet av datamaterial motiveras främst av att Selekt ska implementeras i KSP under år 2010. De parametrar som har behandlats i studien kallas för KAPPA, TAU och LAMBDA samt variablerna RPB_20 och Kostnad.

    Logistisk regression har använts för att undersöka vilken påverkan parametrarna har på den bias (kallad RPB) som införs i skattningarna vid selektiv granskning. En ansats valdes där sambandet mellan responsvariabeln RPB_20 och förklaringsvariablerna KAPPA, TAU och Kostnad studerades separat för olika värden på LAMBDA.

    Vid resultatframställningen indikerades tidigt att valet av värde på LAMBDA inte verkade ha någon nämnvärd betydelse för modellen och i de fortsatta analyserna stärktes denna misstanke och kom att omfatta även KAPPA och TAU. Det var redan från början känt att Kostnad är en viktig variabel att ta hänsyn till och för att undersöka detta närmare konstruerades en modell bestående av ett fjärdegradspolynom med enbart variabeln Kostnad. Modellen lyckades fånga upp huvuddragen av variationen i RPB_20.

    Det går inte att dra generella slutsatser från den studie som här har genomförts. Resultaten visar dock att en modell utan KAPPA, TAU och LAMBDA fungerar för att beskriva variationen i RPB_20.  Valet av värden på KAPPA, TAU och LAMBDA i Selekt är av mindre betydelse. I implementeringsarbetet av Selekt i KSP rekommenderas därför att, förutom RPB, fokusera på variabeln Kostnad för att hitta den mest lämpliga kombinationen av parameterinställningar.

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  • 9.
    Adolfsson, Daniel
    Örebro University, School of Science and Technology.
    Robust large-scale mapping and localization: Combining robust sensing and introspection2023Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    The presence of autonomous systems is rapidly increasing in society and industry. To achieve successful, efficient, and safe deployment of autonomous systems, they must be navigated by means of highly robust localization systems. Additionally, these systems need to localize accurately and efficiently in realtime under adverse environmental conditions, and within considerably diverse and new previously unseen environments.

    This thesis focuses on investigating methods to achieve robust large-scale localization and mapping, incorporating robustness at multiple stages. Specifically, the research explores methods with sensory robustness, utilizing radar, which exhibits tolerance to harsh weather, dust, and variations in lighting conditions. Furthermore, the thesis presents methods with algorithmic robustness, which prevent failures by incorporating introspective awareness of localization quality. This thesis aims to answer the following research questions:

    How can radar data be efficiently filtered and represented for robust radar odometry? How can accurate and robust odometry be achieved with radar? How can localization quality be assessed and leveraged for robust detection of localization failures? How can self-awareness of localization quality be utilized to enhance the robustness of a localization system?

    While addressing these research questions, this thesis makes the following contributions to large-scale localization and mapping: A method for robust and efficient radar processing and state-of-the-art odometry estimation, and a method for self-assessment of localization quality and failure detection in lidar and radar localization. Self-assessment of localization quality is integrated into robust systems for large-scale Simultaneous Localization And Mapping, and rapid global localization in prior maps. These systems leverage self-assessment of localization quality to improve performance and prevent failures in loop closure and global localization, and consequently achieve safe robot localization.

    The methods presented in this thesis were evaluated through comparative assessments of public benchmarks and real-world data collected from various industrial scenarios. These evaluations serve to validate the effectiveness and reliability of the proposed approaches. As a result, this research represents a significant advancement toward achieving highly robust localization capabilities with broad applicability.

    List of papers
    1. Oriented surface points for efficient and accurate radar odometry
    Open this publication in new window or tab >>Oriented surface points for efficient and accurate radar odometry
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    2021 (English)Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper presents an efficient and accurate radar odometry pipeline for large-scale localization. We propose a radar filter that keeps only the strongest reflections per-azimuth that exceeds the expected noise level. The filtered radar data is used to incrementally estimate odometry by registering the current scan with a nearby keyframe. By modeling local surfaces, we were able to register scans by minimizing a point-to-line metric and accurately estimate odometry from sparse point sets, hence improving efficiency. Specifically, we found that a point-to-line metric yields significant improvements compared to a point-to-point metric when matching sparse sets of surface points. Preliminary results from an urban odometry benchmark show that our odometry pipeline is accurate and efficient compared to existing methods with an overall translation error of 2.05%, down from 2.78% from the previously best published method, running at 12.5ms per frame without need of environmental specific training. 

    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:oru:diva-108799 (URN)
    Conference
    Radar Perception for All-Weather Autonomy - Half-Day Workshop at 2021 IEEE International Conference on Robotics and Automation (ICRA 2021), Xi'an, China, May 30 - June 5, 2021
    Funder
    Knowledge FoundationEU, Horizon 2020, 732737
    Available from: 2023-10-09 Created: 2023-10-09 Last updated: 2024-01-02Bibliographically approved
    2. CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry
    Open this publication in new window or tab >>CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry
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    2021 (English)In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), IEEE, 2021, p. 5462-5469Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for large-scale radar odometry estimation. By using a filtering technique that keeps the k strongest returns per azimuth and by additionally filtering the radar data in Cartesian space, we are able to compute a sparse set of oriented surface points for efficient and accurate scan matching. Registration is carried out by minimizing a point-to-line metric and robustness to outliers is achieved using a Huber loss. We were able to additionally reduce drift by jointly registering the latest scan to a history of keyframes and found that our odometry method generalizes to different sensor models and datasets without changing a single parameter. We evaluate our method in three widely different environments and demonstrate an improvement over spatially cross-validated state-of-the-art with an overall translation error of 1.76% in a public urban radar odometry benchmark, running at 55Hz merely on a single laptop CPU thread.

    Place, publisher, year, edition, pages
    IEEE, 2021
    Series
    IEEE International Conference on Intelligent Robots and Systems. Proceedings, ISSN 2153-0858, E-ISSN 2153-0866
    Keywords
    Localization SLAM Mapping Radar
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-94463 (URN)10.1109/IROS51168.2021.9636253 (DOI)000755125504051 ()9781665417143 (ISBN)9781665417150 (ISBN)
    Conference
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), Prague, Czech Republic, (Online Conference), September 27 - October 1, 2021
    Funder
    Knowledge FoundationEU, Horizon 2020, 732737
    Available from: 2021-09-20 Created: 2021-09-20 Last updated: 2024-01-02Bibliographically approved
    3. Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large-Scale Radar Odometry in Diverse Environments
    Open this publication in new window or tab >>Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large-Scale Radar Odometry in Diverse Environments
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    2023 (English)In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 39, no 2, p. 1476-1495Article in journal (Refereed) Published
    Abstract [en]

    This article presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments—outdoors, from urban to woodland, and indoors in warehouses and mines—without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach conservative filtering for efficient and accurate radar odometry (CFEAR), we present an in-depth investigation on a wider range of datasets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar simultaneous localization and mapping (SLAM) and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5 Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160 Hz.

    Place, publisher, year, edition, pages
    IEEE, 2023
    Keywords
    Radar, Sensors, Spinning, Azimuth, Simultaneous localization and mapping, Estimation, Location awareness, Localization, radar odometry, range sensing, SLAM
    National Category
    Computer Sciences Computer Vision and Robotics (Autonomous Systems) Robotics
    Research subject
    Computer and Systems Science; Computer Science
    Identifiers
    urn:nbn:se:oru:diva-103116 (URN)10.1109/tro.2022.3221302 (DOI)000912778500001 ()2-s2.0-85144032264 (Scopus ID)
    Available from: 2023-01-16 Created: 2023-01-16 Last updated: 2023-10-18
    4. BFAR – Bounded False Alarm Rate detector for improved radar odometry estimation
    Open this publication in new window or tab >>BFAR – Bounded False Alarm Rate detector for improved radar odometry estimation
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    2021 (English)Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper presents a new detector for filtering noise from true detections in radar data, which improves the state of the art in radar odometry. Scanning Frequency-Modulated Continuous Wave (FMCW) radars can be useful for localisation and mapping in low visibility, but return a lot of noise compared to (more commonly used) lidar, which makes the detection task more challenging. Our Bounded False-Alarm Rate (BFAR) detector is different from the classical Constant False-Alarm Rate (CFAR) detector in that it applies an affine transformation on the estimated noise level after which the parameters that minimize the estimation error can be learned. BFAR is an optimized combination between CFAR and fixed-level thresholding. Only a single parameter needs to be learned from a training dataset. We apply BFAR tothe use case of radar odometry, and adapt a state-of-the-art odometry pipeline (CFEAR), replacing its original conservative filtering with BFAR. In this way we reduce the state-of-the-art translation/rotation odometry errors from 1.76%/0.5◦/100 m to 1.55%/0.46◦/100 m; an improvement of 12.5%.

    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:oru:diva-108800 (URN)
    Conference
    ICRA
    Funder
    Knowledge Foundation
    Available from: 2023-10-09 Created: 2023-10-09 Last updated: 2024-01-02Bibliographically approved
    5. CorAl – Are the point clouds Correctly Aligned?
    Open this publication in new window or tab >>CorAl – Are the point clouds Correctly Aligned?
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    2021 (English)In: 10th European Conference on Mobile Robots (ECMR 2021), IEEE, 2021, Vol. 10Conference paper, Published paper (Refereed)
    Abstract [en]

    In robotics perception, numerous tasks rely on point cloud registration. However, currently there is no method that can automatically detect misaligned point clouds reliably and without environment-specific parameters. We propose "CorAl", an alignment quality measure and alignment classifier for point cloud pairs, which facilitates the ability to introspectively assess the performance of registration. CorAl compares the joint and the separate entropy of the two point clouds. The separate entropy provides a measure of the entropy that can be expected to be inherent to the environment. The joint entropy should therefore not be substantially higher if the point clouds are properly aligned. Computing the expected entropy makes the method sensitive also to small alignment errors, which are particularly hard to detect, and applicable in a range of different environments. We found that CorAl is able to detect small alignment errors in previously unseen environments with an accuracy of 95% and achieve a substantial improvement to previous methods.

    Place, publisher, year, edition, pages
    IEEE, 2021
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:oru:diva-94464 (URN)10.1109/ECMR50962.2021.9568846 (DOI)000810510000059 ()
    Conference
    10th European Conference on Mobile Robots (ECMR 2021), Bonn, Germany, (Online Conference), August 31 - September 3, 2021
    Funder
    Knowledge FoundationEU, Horizon 2020, 732737 101017274
    Available from: 2021-09-22 Created: 2021-09-22 Last updated: 2024-01-02Bibliographically approved
    6. CorAl: Introspection for robust radar and lidar perception in diverse environments using differential entropy
    Open this publication in new window or tab >>CorAl: Introspection for robust radar and lidar perception in diverse environments using differential entropy
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    2022 (English)In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 155, article id 104136Article in journal (Refereed) Published
    Abstract [en]

    Robust perception is an essential component to enable long-term operation of mobile robots. It depends on failure resilience through reliable sensor data and pre-processing, as well as failure awareness through introspection, for example the ability to self-assess localization performance. This paper presents CorAl: a principled, intuitive, and generalizable method to measure the quality of alignment between pairs of point clouds, which learns to detect alignment errors in a self-supervised manner. CorAl compares the differential entropy in the point clouds separately with the entropy in their union to account for entropy inherent to the scene. By making use of dual entropy measurements, we obtain a quality metric that is highly sensitive to small alignment errors and still generalizes well to unseen environments. In this work, we extend our previous work on lidar-only CorAl to radar data by proposing a two-step filtering technique that produces high-quality point clouds from noisy radar scans. Thus, we target robust perception in two ways: by introducing a method that introspectively assesses alignment quality, and by applying it to an inherently robust sensor modality. We show that our filtering technique combined with CorAl can be applied to the problem of alignment classification, and that it detects small alignment errors in urban settings with up to 98% accuracy, and with up to 96% if trained only in a different environment. Our lidar and radar experiments demonstrate that CorAl outperforms previous methods both on the ETH lidar benchmark, which includes several indoor and outdoor environments, and the large-scale Oxford and MulRan radar data sets for urban traffic scenarios. The results also demonstrate that CorAl generalizes very well across substantially different environments without the need of retraining.

    Place, publisher, year, edition, pages
    Elsevier, 2022
    Keywords
    Radar, Introspection, Localization
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:oru:diva-100756 (URN)10.1016/j.robot.2022.104136 (DOI)000833416900001 ()2-s2.0-85132693467 (Scopus ID)
    Funder
    Knowledge FoundationEuropean Commission, 101017274Vinnova, 2019-05878
    Available from: 2022-08-24 Created: 2022-08-24 Last updated: 2024-01-02Bibliographically approved
    7. TBV Radar SLAM - Trust but Verify Loop Candidates
    Open this publication in new window or tab >>TBV Radar SLAM - Trust but Verify Loop Candidates
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    2023 (English)In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 8, no 6, p. 3613-3620Article in journal (Refereed) Published
    Abstract [en]

    Robust SLAM in large-scale environments requires fault resilience and awareness at multiple stages, from sensing and odometry estimation to loop closure. In this work, we present TBV (Trust But Verify) Radar SLAM, a method for radar SLAM that introspectively verifies loop closure candidates. TBV Radar SLAM achieves a high correct-loop-retrieval rate by combining multiple place-recognition techniques: tightly coupled place similarity and odometry uncertainty search, creating loop descriptors from origin-shifted scans, and delaying loop selection until after verification. Robustness to false constraints is achieved by carefully verifying and selecting the most likely ones from multiple loop constraints. Importantly, the verification and selection are carried out after registration when additional sources of loop evidence can easily be computed. We integrate our loop retrieval and verification method with a robust odometry pipeline within a pose graph framework. By evaluation on public benchmarks we found that TBV Radar SLAM achieves 65% lower error than the previous state of the art. We also show that it generalizes across environments without needing to change any parameters. We provide the open-source implementation at https://github.com/dan11003/tbv_slam_public

    Place, publisher, year, edition, pages
    IEEE, 2023
    Keywords
    SLAM, localization, radar, introspection
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:oru:diva-106249 (URN)10.1109/LRA.2023.3268040 (DOI)000981889200013 ()2-s2.0-85153499426 (Scopus ID)
    Funder
    Vinnova, 2021-04714 2019-05878
    Available from: 2023-06-13 Created: 2023-06-13 Last updated: 2024-01-17Bibliographically approved
    8. Localising Faster: Efficient and precise lidar-based robot localisation in large-scale environments
    Open this publication in new window or tab >>Localising Faster: Efficient and precise lidar-based robot localisation in large-scale environments
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    2020 (English)In: 2020 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2020, p. 4386-4392Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper proposes a novel approach for global localisation of mobile robots in large-scale environments. Our method leverages learning-based localisation and filtering-based localisation, to localise the robot efficiently and precisely through seeding Monte Carlo Localisation (MCL) with a deeplearned distribution. In particular, a fast localisation system rapidly estimates the 6-DOF pose through a deep-probabilistic model (Gaussian Process Regression with a deep kernel), then a precise recursive estimator refines the estimated robot pose according to the geometric alignment. More importantly, the Gaussian method (i.e. deep probabilistic localisation) and nonGaussian method (i.e. MCL) can be integrated naturally via importance sampling. Consequently, the two systems can be integrated seamlessly and mutually benefit from each other. To verify the proposed framework, we provide a case study in large-scale localisation with a 3D lidar sensor. Our experiments on the Michigan NCLT long-term dataset show that the proposed method is able to localise the robot in 1.94 s on average (median of 0.8 s) with precision 0.75 m in a largescale environment of approximately 0.5 km 2.

    Place, publisher, year, edition, pages
    IEEE, 2020
    Series
    IEEE International Conference on Robotics and Automation (ICRA), ISSN 1050-4729, E-ISSN 2577-087X
    Keywords
    Gaussian processes, learning (artificial intelligence), mobile robots, Monte Carlo methods, neural nets, optical radar, path planning, recursive estimation, robot vision, SLAM (robots), precise lidar-based robot localisation, large-scale environments, global localisation, Monte Carlo Localisation, MCL, fast localisation system, deep-probabilistic model, Gaussian process regression, deep kernel, precise recursive estimator, Gaussian method, deep probabilistic localisation, large-scale localisation, largescale environment, time 0.8 s, size 0.75 m, Robots, Neural networks, Three-dimensional displays, Laser radar, Kernel
    National Category
    Robotics
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-88030 (URN)10.1109/ICRA40945.2020.9196708 (DOI)000712319503010 ()2-s2.0-85092712554 (Scopus ID)978-1-7281-7396-2 (ISBN)978-1-7281-7395-5 (ISBN)
    Conference
    2020 IEEE International Conference on Robotics and Automation (ICRA), Paris, France, May 31 - August 31, 2020
    Funder
    EU, Horizon 2020, 732737
    Note

    Funding agency:

    UK Research & Innovation (UKRI)

    Engineering & Physical Sciences Research Council (EPSRC) EP/M019918/1

    Available from: 2021-01-31 Created: 2021-01-31 Last updated: 2024-01-02Bibliographically approved
    9. NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation
    Open this publication in new window or tab >>NDT-Transformer: Large-Scale 3D Point Cloud Localisation using the Normal Distribution Transform Representation
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    2021 (English)In: 2021 IEEE International Conference on Robotics and Automation (ICRA), IEEE, 2021Conference paper, Published paper (Refereed)
    Abstract [en]

    3D point cloud-based place recognition is highly demanded by autonomous driving in GPS-challenged environments and serves as an essential component (i.e. loop-closure detection) in lidar-based SLAM systems. This paper proposes a novel approach, named NDT-Transformer, for real-time and large-scale place recognition using 3D point clouds. Specifically, a 3D Normal Distribution Transform (NDT) representation is employed to condense the raw, dense 3D point cloud as probabilistic distributions (NDT cells) to provide the geometrical shape description. Then a novel NDT-Transformer network learns a global descriptor from a set of 3D NDT cell representations. Benefiting from the NDT representation and NDT-Transformer network, the learned global descriptors are enriched with both geometrical and contextual information. Finally, descriptor retrieval is achieved using a query-database for place recognition. Compared to the state-of-the-art methods, the proposed approach achieves an improvement of 7.52% on average top 1 recall and 2.73% on average top 1% recall on the Oxford Robotcar benchmark.

    Place, publisher, year, edition, pages
    IEEE, 2021
    Series
    IEEE International Conference on Robotics and Automation (ICRA), ISSN 1050-4729, E-ISSN 2577-087X
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-96652 (URN)10.1109/ICRA48506.2021.9560932 (DOI)000765738804041 ()2-s2.0-85124680724 (Scopus ID)9781728190778 (ISBN)9781728190785 (ISBN)
    Conference
    2021 IEEE International Conference on Robotics and Automation (ICRA 2021), Xi'an, China, May 30 - June 5, 2021
    Funder
    EU, Horizon 2020, 732737
    Note

    Funding agencies:

    UK Research & Innovation (UKRI)

    Engineering & Physical Sciences Research Council (EPSRC) EP/R026092/1  

    Royal Society of London European Commission RGS202432

    Available from: 2022-01-24 Created: 2022-01-24 Last updated: 2024-01-02Bibliographically approved
    10. Improving Localisation Accuracy using Submaps in warehouses
    Open this publication in new window or tab >>Improving Localisation Accuracy using Submaps in warehouses
    2018 (English)Conference paper, Oral presentation with published abstract (Other academic)
    Abstract [en]

    This paper presents a method for localisation in hybrid metric-topological maps built using only local information that is, only measurements that were captured by the robot when it was in a nearby location. The motivation is that observations are typically range and viewpoint dependent and that a map a discrete map representation might not be able to explain the full structure within a voxel. The localisation system uses a method to select submap based on how frequently and where from each submap was updated. This allow the system to select the most descriptive submap, thereby improving the localisation and increasing performance by up to 40%.

    National Category
    Robotics
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-71844 (URN)
    Conference
    IEEE/RSJ Int. Conf. on Intelligent Robots and Systems (IROS), Workshop on Robotics for Logistics in Warehouses and Environments Shared with Humans, Madrid, Spain, October 5, 2018
    Projects
    Iliad
    Available from: 2019-01-28 Created: 2019-01-28 Last updated: 2024-01-02Bibliographically approved
    11. A Submap per Perspective: Selecting Subsets for SuPer Mapping that Afford Superior Localization Quality
    Open this publication in new window or tab >>A Submap per Perspective: Selecting Subsets for SuPer Mapping that Afford Superior Localization Quality
    Show others...
    2019 (English)In: 2019 European Conference on Mobile Robots (ECMR), IEEE, 2019Conference paper, Published paper (Refereed)
    Abstract [en]

    This paper targets high-precision robot localization. We address a general problem for voxel-based map representations that the expressiveness of the map is fundamentally limited by the resolution since integration of measurements taken from different perspectives introduces imprecisions, and thus reduces localization accuracy.We propose SuPer maps that contain one Submap per Perspective representing a particular view of the environment. For localization, a robot then selects the submap that best explains the environment from its perspective. We propose SuPer mapping as an offline refinement step between initial SLAM and deploying autonomous robots for navigation. We evaluate the proposed method on simulated and real-world data that represent an important use case of an industrial scenario with high accuracy requirements in an repetitive environment. Our results demonstrate a significantly improved localization accuracy, up to 46% better compared to localization in global maps, and up to 25% better compared to alternative submapping approaches.

    Place, publisher, year, edition, pages
    IEEE, 2019
    National Category
    Computer Sciences
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-79739 (URN)10.1109/ECMR.2019.8870941 (DOI)000558081900037 ()2-s2.0-85074443858 (Scopus ID)978-1-7281-3605-9 (ISBN)
    Conference
    European Conference on Mobile Robotics (ECMR), Prague, Czech Republic, September 4-6, 2019
    Funder
    EU, Horizon 2020, 732737Knowledge Foundation
    Available from: 2020-02-03 Created: 2020-02-03 Last updated: 2024-01-02Bibliographically approved
    12. Incorporating Ego-motion Uncertainty Estimates in Range Data Registration
    Open this publication in new window or tab >>Incorporating Ego-motion Uncertainty Estimates in Range Data Registration
    Show others...
    2017 (English)In: 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Institute of Electrical and Electronics Engineers (IEEE), 2017, p. 1389-1395Conference paper, Published paper (Refereed)
    Abstract [en]

    Local scan registration approaches commonlyonly utilize ego-motion estimates (e.g. odometry) as aninitial pose guess in an iterative alignment procedure. Thispaper describes a new method to incorporate ego-motionestimates, including uncertainty, into the objective function of aregistration algorithm. The proposed approach is particularlysuited for feature-poor and self-similar environments,which typically present challenges to current state of theart registration algorithms. Experimental evaluation showssignificant improvements in accuracy when using data acquiredby Automatic Guided Vehicles (AGVs) in industrial productionand warehouse environments.

    Place, publisher, year, edition, pages
    Institute of Electrical and Electronics Engineers (IEEE), 2017
    Series
    Proceedings of the ... IEEE/RSJ International Conference on Intelligent Robots and Systems, ISSN 2153-0858, E-ISSN 2153-0866
    National Category
    Robotics
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-62803 (URN)10.1109/IROS.2017.8202318 (DOI)000426978201108 ()2-s2.0-85041958720 (Scopus ID)978-1-5386-2682-5 (ISBN)978-1-5386-2683-2 (ISBN)
    Conference
    IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2017), Vancouver, Canada, September 24–28, 2017
    Projects
    Semantic RobotsILIAD
    Funder
    Knowledge FoundationEU, Horizon 2020, 732737
    Available from: 2017-11-24 Created: 2017-11-24 Last updated: 2024-01-02Bibliographically approved
    Download full text (pdf)
    Robust large-scale mapping and localization: Combining robust sensing and introspection
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  • 10.
    Adolfsson, Daniel
    et al.
    Örebro University, School of Science and Technology.
    Castellano-Quero, Manuel
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    CorAl: Introspection for robust radar and lidar perception in diverse environments using differential entropy2022In: Robotics and Autonomous Systems, ISSN 0921-8890, E-ISSN 1872-793X, Vol. 155, article id 104136Article in journal (Refereed)
    Abstract [en]

    Robust perception is an essential component to enable long-term operation of mobile robots. It depends on failure resilience through reliable sensor data and pre-processing, as well as failure awareness through introspection, for example the ability to self-assess localization performance. This paper presents CorAl: a principled, intuitive, and generalizable method to measure the quality of alignment between pairs of point clouds, which learns to detect alignment errors in a self-supervised manner. CorAl compares the differential entropy in the point clouds separately with the entropy in their union to account for entropy inherent to the scene. By making use of dual entropy measurements, we obtain a quality metric that is highly sensitive to small alignment errors and still generalizes well to unseen environments. In this work, we extend our previous work on lidar-only CorAl to radar data by proposing a two-step filtering technique that produces high-quality point clouds from noisy radar scans. Thus, we target robust perception in two ways: by introducing a method that introspectively assesses alignment quality, and by applying it to an inherently robust sensor modality. We show that our filtering technique combined with CorAl can be applied to the problem of alignment classification, and that it detects small alignment errors in urban settings with up to 98% accuracy, and with up to 96% if trained only in a different environment. Our lidar and radar experiments demonstrate that CorAl outperforms previous methods both on the ETH lidar benchmark, which includes several indoor and outdoor environments, and the large-scale Oxford and MulRan radar data sets for urban traffic scenarios. The results also demonstrate that CorAl generalizes very well across substantially different environments without the need of retraining.

  • 11.
    Adolfsson, Daniel
    et al.
    Örebro University, School of Science and Technology.
    Karlsson, Mattias
    MRO Lab of the AASS Research Centre, Örebro University, Örebro, Sweden.
    Kubelka, Vladimír
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    TBV Radar SLAM - Trust but Verify Loop Candidates2023In: IEEE Robotics and Automation Letters, E-ISSN 2377-3766, Vol. 8, no 6, p. 3613-3620Article in journal (Refereed)
    Abstract [en]

    Robust SLAM in large-scale environments requires fault resilience and awareness at multiple stages, from sensing and odometry estimation to loop closure. In this work, we present TBV (Trust But Verify) Radar SLAM, a method for radar SLAM that introspectively verifies loop closure candidates. TBV Radar SLAM achieves a high correct-loop-retrieval rate by combining multiple place-recognition techniques: tightly coupled place similarity and odometry uncertainty search, creating loop descriptors from origin-shifted scans, and delaying loop selection until after verification. Robustness to false constraints is achieved by carefully verifying and selecting the most likely ones from multiple loop constraints. Importantly, the verification and selection are carried out after registration when additional sources of loop evidence can easily be computed. We integrate our loop retrieval and verification method with a robust odometry pipeline within a pose graph framework. By evaluation on public benchmarks we found that TBV Radar SLAM achieves 65% lower error than the previous state of the art. We also show that it generalizes across environments without needing to change any parameters. We provide the open-source implementation at https://github.com/dan11003/tbv_slam_public

    The full text will be freely available from 2025-06-01 00:00
  • 12.
    Adolfsson, Daniel
    et al.
    Örebro University, School of Science and Technology.
    Lowry, Stephanie
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    A Submap per Perspective: Selecting Subsets for SuPer Mapping that Afford Superior Localization Quality2019In: 2019 European Conference on Mobile Robots (ECMR), IEEE, 2019Conference paper (Refereed)
    Abstract [en]

    This paper targets high-precision robot localization. We address a general problem for voxel-based map representations that the expressiveness of the map is fundamentally limited by the resolution since integration of measurements taken from different perspectives introduces imprecisions, and thus reduces localization accuracy.We propose SuPer maps that contain one Submap per Perspective representing a particular view of the environment. For localization, a robot then selects the submap that best explains the environment from its perspective. We propose SuPer mapping as an offline refinement step between initial SLAM and deploying autonomous robots for navigation. We evaluate the proposed method on simulated and real-world data that represent an important use case of an industrial scenario with high accuracy requirements in an repetitive environment. Our results demonstrate a significantly improved localization accuracy, up to 46% better compared to localization in global maps, and up to 25% better compared to alternative submapping approaches.

    Download full text (pdf)
    A Submap per Perspective - Selecting Subsets for SuPer Mapping that Afford Superior Localization Quality
  • 13.
    Adolfsson, Daniel
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Alhashimi, Anas
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry2021In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021), IEEE, 2021, p. 5462-5469Conference paper (Refereed)
    Abstract [en]

    This paper presents the accurate, highly efficient, and learning-free method CFEAR Radarodometry for large-scale radar odometry estimation. By using a filtering technique that keeps the k strongest returns per azimuth and by additionally filtering the radar data in Cartesian space, we are able to compute a sparse set of oriented surface points for efficient and accurate scan matching. Registration is carried out by minimizing a point-to-line metric and robustness to outliers is achieved using a Huber loss. We were able to additionally reduce drift by jointly registering the latest scan to a history of keyframes and found that our odometry method generalizes to different sensor models and datasets without changing a single parameter. We evaluate our method in three widely different environments and demonstrate an improvement over spatially cross-validated state-of-the-art with an overall translation error of 1.76% in a public urban radar odometry benchmark, running at 55Hz merely on a single laptop CPU thread.

    Download full text (pdf)
    CFEAR Radarodometry - Conservative Filtering for Efficient and Accurate Radar Odometry
  • 14.
    Adolfsson, Daniel
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Alhashimi, Anas
    Örebro University, Örebro, Sweden; Computer Engineering Department, University of Baghdad, Baghdad, Iraq.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lidar-Level Localization With Radar? The CFEAR Approach to Accurate, Fast, and Robust Large-Scale Radar Odometry in Diverse Environments2023In: IEEE Transactions on robotics, ISSN 1552-3098, E-ISSN 1941-0468, Vol. 39, no 2, p. 1476-1495Article in journal (Refereed)
    Abstract [en]

    This article presents an accurate, highly efficient, and learning-free method for large-scale odometry estimation using spinning radar, empirically found to generalize well across very diverse environments—outdoors, from urban to woodland, and indoors in warehouses and mines—without changing parameters. Our method integrates motion compensation within a sweep with one-to-many scan registration that minimizes distances between nearby oriented surface points and mitigates outliers with a robust loss function. Extending our previous approach conservative filtering for efficient and accurate radar odometry (CFEAR), we present an in-depth investigation on a wider range of datasets, quantifying the importance of filtering, resolution, registration cost and loss functions, keyframe history, and motion compensation. We present a new solving strategy and configuration that overcomes previous issues with sparsity and bias, and improves our state-of-the-art by 38%, thus, surprisingly, outperforming radar simultaneous localization and mapping (SLAM) and approaching lidar SLAM. The most accurate configuration achieves 1.09% error at 5 Hz on the Oxford benchmark, and the fastest achieves 1.79% error at 160 Hz.

    Download full text (pdf)
    Lidar-level localization with radar? The CFEAR approach to accurate, fast and robust large-scale radar odometry in diverse environments
  • 15.
    Adolfsson, Daniel
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Alhashimi, Anas
    School of Science and Technology, Örebro University, Örebro, Sweden.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Oriented surface points for efficient and accurate radar odometry2021Conference paper (Refereed)
    Abstract [en]

    This paper presents an efficient and accurate radar odometry pipeline for large-scale localization. We propose a radar filter that keeps only the strongest reflections per-azimuth that exceeds the expected noise level. The filtered radar data is used to incrementally estimate odometry by registering the current scan with a nearby keyframe. By modeling local surfaces, we were able to register scans by minimizing a point-to-line metric and accurately estimate odometry from sparse point sets, hence improving efficiency. Specifically, we found that a point-to-line metric yields significant improvements compared to a point-to-point metric when matching sparse sets of surface points. Preliminary results from an urban odometry benchmark show that our odometry pipeline is accurate and efficient compared to existing methods with an overall translation error of 2.05%, down from 2.78% from the previously best published method, running at 12.5ms per frame without need of environmental specific training. 

  • 16.
    Adolfsson, Daniel
    et al.
    Örebro University, School of Science and Technology.
    Magnusson, Martin
    Örebro University, School of Science and Technology.
    Liao, Qianfang
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    CorAl – Are the point clouds Correctly Aligned?2021In: 10th European Conference on Mobile Robots (ECMR 2021), IEEE, 2021, Vol. 10Conference paper (Refereed)
    Abstract [en]

    In robotics perception, numerous tasks rely on point cloud registration. However, currently there is no method that can automatically detect misaligned point clouds reliably and without environment-specific parameters. We propose "CorAl", an alignment quality measure and alignment classifier for point cloud pairs, which facilitates the ability to introspectively assess the performance of registration. CorAl compares the joint and the separate entropy of the two point clouds. The separate entropy provides a measure of the entropy that can be expected to be inherent to the environment. The joint entropy should therefore not be substantially higher if the point clouds are properly aligned. Computing the expected entropy makes the method sensitive also to small alignment errors, which are particularly hard to detect, and applicable in a range of different environments. We found that CorAl is able to detect small alignment errors in previously unseen environments with an accuracy of 95% and achieve a substantial improvement to previous methods.

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    CorAl – Are the point clouds Correctly Aligned?
  • 17.
    Adolfsson, Emma
    et al.
    Örebro University, School of Medical Sciences. Department of Laboratory Medicine.
    Jonasson, Jon
    Department of Laboratory Medicine, Örebro University Hospital, Örebro, Sweden.
    Kashyap, Aniruddh
    Department of Laboratory Medicine, Örebro University Hospital, Örebro, Sweden.
    Nordensköld, Anna
    Department of Cardiology, Faculty of Medicine and Health, Örebro University Hospital, Örebro, Sweden.
    Green, Anna
    Örebro University, School of Medical Sciences. Örebro University Hospital. Department of Laboratory Medicine.
    CNV-Z; a new tool for detecting copy number variation in next generation sequencing data2023In: SoftwareX, E-ISSN 2352-7110, Vol. 24, article id 101530Article in journal (Refereed)
    Abstract [en]

    We developed an efficient approach to diagnostic copy number analysis of targeted gene panel or whole exome sequence (WES) data. Here we present CNV-Z as a new tool for detection of copy number variants (CNVs). Deletions and duplications of chromosomal regions are widely implicated in both genomic evolution and genetic disorders. However, calling CNVs from targeted or exome sequence data is challenging. In most cases, the copy number of a chromosomal region is estimated as the depth of reads mapping to a certain bin or sliding window divided by the expected number of reads derived from a set of reference samples. This approach will inevitably miss smaller CNVs on an irregular basis, and quite frequently results in a disturbing number of false positive CNVs. We developed an alternative approach to detect CNVs based on deviation from expected read depth per position, instead of region. Cautiously used, the cohort of samples in the same run will do as a reference. With appropriate filtering, given high quality DNA and a set of suitable reference samples, CNV-Z detects CNVs ranging in length from one nucleotide to an entire chromosome, with few false positives. Performance is proved by benchmarking using both in-house targeted gene panel NGS data and a publicly available NGS dataset, both sets with multiplex ligation-dependent amplification probe (MLPA) validated CNVs. The outcome shows that CNV-Z detects single- and multi-exonic CNVs with high specificity and sensitivity using different kind of NGS data. On gene level, CNV-Z shows both excellent sensitivity and specificity. Compared to competing CNV callers, CNV-Z shows higher specificity and positive predictive value for detecting exonic CNVs.

  • 18.
    Afshar, Sara
    et al.
    Mälardalen University, Västerås, Sweden.
    Nemati, Farhang
    Mälardalen University, Västerås, Sweden.
    Nolte, Thomas
    Mälardalen University, Västerås, Sweden.
    Resource Sharing under Multiprocessor Semi-Partitioned Scheduling2012In: 2012 IEEE International Conference on Embedded and Real-Time Computing Systems and Applications: Proceedings, IEEE, 2012, p. 290-299Conference paper (Refereed)
    Abstract [en]

    Semi-partitioned scheduling has become the subject of recent interest for multiprocessors due to better utilization results, compared to conventional global and partitioned scheduling algorithms. Under semi-partitioned scheduling, a major group of tasks are assigned to fixed processors while a low number of tasks are allocated to more than one processor. Various task assigning techniques have recently been proposed in a semi-partitioned environment. However, a synchronization mechanism for resource sharing among tasks in semi-partitioned scheduling has not yet been investigated. In this paper we propose and evaluate two methods for handling resource sharing under semi-partitioned scheduling in multiprocessor platforms. The main challenge addressed in this paper is to serve the resource requests of tasks that are assigned to different processors.

  • 19.
    Afshar, Sara
    et al.
    Mälardalen University, Västerås, Sweden.
    Nemati, Farhang
    Mälardalen University, Västerås, Sweden.
    Nolte, Thomas
    Mälardalen University, Västerås, Sweden.
    Towards Resource Sharing under Multiprocessor Semi-Partitioned Scheduling2012In: 7th IEEE International Symposium on Industrial Embedded Systems (SIES'12): Conference Proceedings, IEEE, 2012, p. 315-318Conference paper (Refereed)
    Abstract [en]

    Semi-partitioned scheduling has been the subject of recent interest, compared with conventional global and partitioned scheduling algorithms for multiprocessors, due to better utilization results. In semi-partitioned scheduling most tasks are assigned to fixed processors while a low number of tasks are split up and allocated to different processors. Various techniques have recently been proposed to assign tasks in a semi-partitioned environment. However, an appropriate resource sharing mechanism for handling the resource requests between tasks in semi-partitioned scheduling has not yet been investigated. In this paper we propose two methods for handling resource sharing under semi-partitioned scheduling in multiprocessor platforms. The main challenge is to handle the resource requests of tasks that are split over multiple processors.

  • 20.
    AGALOMBA, CHRISTINE AFANDI
    Örebro University, Örebro University School of Business.
    Factors contributing to failure of egovernment projects in developing countries: a literature review2012Independent thesis Advanced level (degree of Master (Two Years)), 10 credits / 15 HE creditsStudent thesis
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    fulltext
  • 21.
    Agalomba, Christine Afandi
    et al.
    Örebro University, Swedish Business School at Örebro University.
    Bakibinga, Stella
    Örebro University, Swedish Business School at Örebro University.
    A Review of Telecentre Literature: Sustainability, Impact and Best practices2010Independent thesis Advanced level (degree of Master (One Year)), 10 credits / 15 HE creditsStudent thesis
  • 22.
    Aghanavesi, Somayeh
    et al.
    Department of Computer Engineering, Dalarna University, Borlänge, Sweden.
    Bergquist, Filip
    Department of Pharmacology, Institute of Neuroscience and Physiology, Gothenburg University, Gothenburg, Sweden.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Senek, Marina
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    Motion sensor-based assessment of Parkinson's disease motor symptoms during leg agility tests: results from levodopa challenge2020In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 24, no 1, p. 111-118Article in journal (Refereed)
    Abstract [en]

    Parkinson's disease (PD) is a degenerative, progressive disorder of the central nervous system that mainly affects motor control. The aim of this study was to develop data-driven methods and test their clinimetric properties to detect and quantify PD motor states using motion sensor data from leg agility tests. Nineteen PD patients were recruited in a levodopa single dose challenge study. PD patients performed leg agility tasks while wearing motion sensors on their lower extremities. Clinical evaluation of video recordings was performed by three movement disorder specialists who used four items from the motor section of the Unified PD Rating Scale (UPDRS), the treatment response scale (TRS) and a dyskinesia score. Using the sensor data, spatiotemporal features were calculated and relevant features were selected by feature selection. Machine learning methods like support vector machines (SVM), decision trees and linear regression, using 10-fold cross validation were trained to predict motor states of the patients. SVM showed the best convergence validity with correlation coefficients of 0.81 to TRS, 0.83 to UPDRS #31 (body bradykinesia and hypokinesia), 0.78 to SUMUPDRS (the sum of the UPDRS items: #26-leg agility, #27-arising from chair and #29-gait), and 0.67 to dyskinesia. Additionally, the SVM-based scores had similar test-retest reliability in relation to clinical ratings. The SVM-based scores were less responsive to treatment effects than the clinical scores, particularly with regards to dyskinesia. In conclusion, the results from this study indicate that using motion sensors during leg agility tests may lead to valid and reliable objective measures of PD motor symptoms.

  • 23.
    Aghanavesi, Somayeh
    et al.
    Computer Engineering, School of Technology and Business Studies, Borlänge, Dalarna University, Sweden.
    Bergquist, Filip
    Dept. of Pharmacology, University of Gothenburg, Gothenburg, Sweden.
    Nyholm, Dag
    Dept. of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Senek, Marina
    Dept. of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    Objective assessment of Parkinson’s disease motor symptoms during leg agility test using motion sensors2018Conference paper (Refereed)
    Abstract [en]

    Title: Objective assessment of Parkinson’s disease motor symptoms during leg agility test using motion sensors

    Objective: To develop and evaluate machine learning methods for assessment of Parkinson’s disease (PD) motor symptoms using leg agility (LA) data collected with motion sensors during a single dose experiment.

    Background: Nineteen advanced PD patients (Gender: 14 males and 5 females, mean age: 71.4, mean years with PD: 9.7, mean years with levodopa: 9.5) were recruited in a single center, open label, single dose clinical trial in Sweden [1].

    Methods: The patients performed up to 15 LA tasks while wearing motions sensors on their foot ankle. They performed tests at pre-defined time points starting from baseline, at the time they received a morning dose (150% of their levodopa equivalent morning dose), and at follow-up time points until the medication wore off. The patients were video recorded while performing the motor tasks. and three movement disorder experts rated the observed motor symptoms using 4 items from the Unified PD Rating Scale (UPDRS) motor section including UPDRS #26 (leg agility), UPDRS #27 (Arising from chair), UPDRS #29 (Gait), UPDRS #31 (Body Bradykinesia and Hypokinesia), and dyskinesia scale. In addition, they rated the overall mobility of the patients using Treatment Response Scale (TRS), ranging from -3 (very off) to 3 (very dyskinetic). Sensors data were processed and their quantitative measures were used to develop machine learning methods, which mapped them to the mean ratings of the three raters. The quality of measurements of the machine learning methods was assessed by convergence validity, test-retest reliability and sensitivity to treatment.

    Results: Results from the 10-fold cross validation showed good convergent validity of the machine learning methods (Support Vector Machines, SVM) with correlation coefficients of 0.81 for TRS, 0.78 for UPDRS #26, 0.69 for UPDRS #27, 0.78 for UPDRS #29, 0.83 for UPDRS #31, and 0.67 for dyskinesia scale (P<0.001). There were good correlations between scores produced by the methods during the first (baseline) and second tests with coefficients ranging from 0.58 to 0.96, indicating good test-retest reliability. The machine learning methods had lower sensitivity than mean clinical ratings (Figure. 1).

    Conclusions: The presented methodology was able to assess motor symptoms in PD well, comparable to movement disorder experts. The leg agility test did not reflect treatment related changes.

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    Objective assessment of Parkinson’s disease motor symptoms during leg agility test using motion sensors
  • 24.
    Aghanavesi, Somayeh
    et al.
    Computer Engineering, School of Technology and Business Studies, Dalarna University, Borlänge, Sweden.
    Filip, Bergquist
    Dept. of Pharmacology, University of Gothenburg, Gothenbrug, Sweden.
    Nyholm, Dag
    Dept. of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Senek, Marina
    Dept. of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    Feasibility of a multi-sensor data fusion method for assessment of Parkinson’s disease motor symptoms2018Conference paper (Other academic)
    Abstract [en]

    Title: Feasibility of a multi-sensor data fusion method for assessment of Parkinson’s disease motor symptoms

    Objective: To assess the feasibility of measuring Parkinson’s disease (PD) motor symptoms with a multi-sensor data fusion method. More specifically, the aim is to assess validity, reliability and sensitivity to treatment of the methods.

    Background: Data from 19 advanced PD patients (Gender: 14 males and 5 females, mean age: 71.4, mean years with PD: 9.7, mean years with levodopa: 9.5) were collected in a single center, open label, single dose clinical trial in Sweden [1].

    Methods: The patients performed leg agility and 2-5 meter straight walking tests while wearing motion sensors on their limbs. They performed the tests at baseline, at the time they received the morning dose, and at pre-specified time points until the medication wore off. While performing the tests the patients were video recorded. The videos were observed by three movement disorder specialists who rated the symptoms using a treatment response scale (TRS), ranging from -3 (very off) to 3 (very dyskinetic). The sensor data consisted of lower limb data during leg agility, upper limb data during walking, and lower limb data during walking. Time series analysis was performed on the raw sensor data extracted from 17 patients to derive a set of quantitative measures, which were then used during machine learning to be mapped to mean ratings of the three raters on the TRS scale. Combinations of data were tested during the machine learning procedure.

    Results: Using data from both tests, the Support Vector Machines (SVM) could predict the motor states of the patients on the TRS scale with a good agreement in relation to the mean ratings of the three raters (correlation coefficient = 0.92, root mean square error = 0.42, p<0.001). Additionally, there was good test-retest reliability of the SVM scores during baseline and second tests with intraclass-correlation coefficient of 0.84. Sensitivity to treatment for SVM was good (Figure 1), indicating its ability to detect changes in motor symptoms. The upper limb data during walking was more informative than lower limb data during walking since SVMs had higher correlation coefficient to mean ratings.  

    Conclusions: The methodology demonstrates good validity, reliability, and sensitivity to treatment. This indicates that it could be useful for individualized optimization of treatments among PD patients, leading to an improvement in health-related quality of life.

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    Feasibility of a multi-sensor data fusion method for assessment of Parkinson’s disease motor symptoms
  • 25.
    Aghanavesi, Somayeh
    et al.
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    Dougherty, Mark
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Westin, Jerker
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Verification of a Method for Measuring Parkinson’s Disease Related Temporal Irregularity in Spiral Drawings2017In: Sensors, E-ISSN 1424-8220, Vol. 17, no 10, article id 2341Article in journal (Refereed)
    Abstract [en]

    Parkinson’s disease (PD) is a progressive movement disorder caused by the death of dopamine-producing cells in the midbrain. There is a need for frequent symptom assessment, since the treatment needs to be individualized as the disease progresses. The aim of this paper was to verify and further investigate the clinimetric properties of an entropy-based method for measuring PD-related upper limb temporal irregularities during spiral drawing tasks. More specifically, properties of a temporal irregularity score (TIS) for patients at different stages of PD, and medication time points were investigated. Nineteen PD patients and 22 healthy controls performed repeated spiral drawing tasks on a smartphone. Patients performed the tests before a single levodopa dose and at specific time intervals after the dose was given. Three movement disorder specialists rated videos of the patients based on the unified PD rating scale (UPDRS) and the Dyskinesia scale. Differences in mean TIS between the groups of patients and healthy subjects were assessed. Test-retest reliability of the TIS was measured. The ability of TIS to detect changes from baseline (before medication) to later time points was investigated. Correlations between TIS and clinical rating scores were assessed. The mean TIS was significantly different between healthy subjects and patients in advanced groups (p-value = 0.02). Test-retest reliability of TIS was good with Intra-class Correlation Coefficient of 0.81. When assessing changes in relation to treatment, TIS contained some information to capture changes from Off to On and wearing off effects. However, the correlations between TIS and clinical scores (UPDRS and Dyskinesia) were weak. TIS was able to differentiate spiral drawings drawn by patients in an advanced stage from those drawn by healthy subjects, and TIS had good test-retest reliability. TIS was somewhat responsive to single-dose levodopa treatment. Since TIS is an upper limb high-frequency-based measure, it cannot be detected during clinical assessment.

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  • 26.
    Aghanavesi, Somayeh
    et al.
    Computer Engineering, School of Technology and Business Studies, Dalarna University, Borlänge, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    Westin, Jerker
    Computer Engineering, School of Technology and Business Studies, Dalarna University, Borlänge, Sweden.
    Measuring temporal irregularity in spiral drawings of patients with Parkinson’s disease2017In: Abstracts of the 21st International Congress of Parkinson's Disease and Movement Disorders, John Wiley & Sons, 2017, Vol. 32, p. s252-s252, article id 654Conference paper (Other (popular science, discussion, etc.))
    Abstract [en]

    Objective: The aim of this work is to evaluate clinimetric properties of a method for measuring Parkinson’s disease (PD) upper limb temporal irregularities during spiral drawing tasks.

    Background: Basal ganglia fluctuations of PD patients are associated with motor symptoms and relating them to objective sensor-based measures may facilitate the assessment of temporal irregularities, which could be difficult to be assessed visually. The present study investigated the upper limb temporal irregularity of patients at different stages of PD and medication time points.

    Methods: Nineteen PD patients and 22 healthy controls performed repeated spiral drawing tasks on a smartphone. Patients performed the tests before a single levodopa dose and at specific time intervals after the dose was given. Three movement disorder specialists rated the videos of patients' performance according to six items of UPDRS-III, dyskinesia (Dys), and Treatment Response Scale (TRS). A temporal irregularity score (TIS) was developed using approximate entropy (ApEn) method. Differences in mean TIS between two groups of patients and healthy subjects, and also across four subject groups: early, intermediate, advanced patients and, healthy subjects were assessed. The relative ability of TIS to detect changes from baseline (no medication) to later time points when patients were on medication was assessed. Correlations between TIS and clinical rating scales were assessed by Pearson correlation coefficients and test-retest reliability of TIS was measured by intra-class correlation coefficients (ICC).

    Results: The mean TIS was significantly different between healthy subjects and patients (P<0.0001). When assessing the changes in relation to treatment, clinical-based scores (TRS and Dys) had better responsiveness than TIS. However, the TIS was able to capture changes from Off to On, and the wearing off effects. Correlations between TIS and clinical scales were low indicating poor validity. Test-retest reliability correlation coefficient of the mean TIS was good (ICC=0.67).

    Conclusions: Our study found that TIS was able to differentiate spiral drawings drawn by patients from those drawn by healthy subjects. In addition, TIS could capture changes throughout the levodopa cycle.TIS was weakly correlated to clinical ratings indicating that TIS measures high frequency upper limb temporal irregularities that could be difficult to be detected during clinical observations.

  • 27.
    Aghanavesi, Somayeh
    et al.
    Computer Engineering, School of Technology and Business Studies, Dalarna University, Falun, Sweden.
    Nyholm, Dag
    Dept. of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Senek, Marina
    Dept. of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Bergquist, Filip
    Dept. of Pharmacology, University of Gothenburg, Gothenburg, Sweden.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business. Computer Engineering, School of Technology and Business Studies, Dalarna University, Falun, Sweden.
    A smartphone-based system to quantify dexterity in Parkinson's disease patients2017In: Informatics in Medicine Unlocked, ISSN 2352-9148, Vol. 9, p. 11-17Article in journal (Refereed)
    Abstract [en]

    Objectives

    The aim of this paper is to investigate whether a smartphone-based system can be used to quantify dexterity in Parkinson's disease (PD). More specifically, the aim was to develop data-driven methods to quantify and characterize dexterity in PD.

    Methods

    Nineteen advanced PD patients and 22 healthy controls participated in a clinical trial in Uppsala, Sweden. The subjects were asked to perform tapping and spiral drawing tests using a smartphone. Patients performed the tests before, and at pre-specified time points after they received 150% of their usual levodopa morning dose. Patients were video recorded and their motor symptoms were assessed by three movement disorder specialists using three Unified PD Rating Scale (UPDRS) motor items from part III, the dyskinesia scoring and the treatment response scale (TRS). The raw tapping and spiral data were processed and analyzed with time series analysis techniques to extract 37 spatiotemporal features. For each of the five scales, separate machine learning models were built and tested by using principal components of the features as predictors and mean ratings of the three specialists as target variables.

    Results

    There were weak to moderate correlations between smartphone-based scores and mean ratings of UPDRS item #23 (0.52; finger tapping), UPDRS #25 (0.47; rapid alternating movements of hands), UPDRS #31 (0.57; body bradykinesia and hypokinesia), sum of the three UPDRS items (0.46), dyskinesia (0.64), and TRS (0.59). When assessing the test-retest reliability of the scores it was found that, in general, the clinical scores had better test-retest reliability than the smartphone-based scores. Only the smartphone-based predicted scores on the TRS and dyskinesia scales had good repeatability with intra-class correlation coefficients of 0.51 and 0.84, respectively. Clinician-based scores had higher effect sizes than smartphone-based scores indicating a better responsiveness in detecting changes in relation to treatment interventions. However, the first principal component of the 37 features was able to capture changes throughout the levodopa cycle and had trends similar to the clinical TRS and dyskinesia scales. Smartphone-based scores differed significantly between patients and healthy controls.

    Conclusions

    Quantifying PD motor symptoms via instrumented, dexterity tests employed in a smartphone is feasible and data from such tests can also be used for measuring treatment-related changes in patients.

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  • 28.
    Aghanavesi, Somayeh
    et al.
    Department of Computer Engineering, Dalarna University, Falun, Sweden.
    Westin, Jerker
    Department of Computer Engineering, Dalarna University, Falun, Sweden.
    Bergquist, Filip
    Department of Pharmacology at Institute of Neuroscience and Physiology, University of Gothenburg, Gothenburg, Sweden.
    Nyholm, Dag
    Department of Neuroscience, Neurology at Uppsala University, Uppsala, Sweden.
    Askmark, Håkan
    Department of Neuroscience, Neurology at Uppsala University, Uppsala, Sweden.
    Aquilonius, Sten Magnus
    Department of Neuroscience, Neurology at Uppsala University, Uppsala, Sweden.
    Constantinescu, Radu
    Department of Clinical Neuroscience, University of Gothenburg, Gothenburg, Sweden.
    Medvedev, Alexander
    Department of Information Technology, at Uppsala University, Uppsala, Sweden.
    Spira, Jack
    Sensidose AB, Sollentuna, Sweden.
    Ohlsson, Fredrik
    Department of Sensor Systems at Acreo Swedish ICT, Kista, Sweden.
    Thomas, Ilias
    Department of Statistics, Dalarna University, Falun, Sweden.
    Ericsson, Anders
    Department of Clinical Neuroscience and Rehabilitation, University of Gothenburg, Gothenburg, Sweden.
    Johansson Buvarp, Dongni
    Irisity AB, Gothenburg.
    Memedi, Mevludin
    Örebro University, Örebro University School of Business.
    A multiple motion sensors index for motor state quantification in Parkinson's disease2020In: Computer Methods and Programs in Biomedicine, ISSN 0169-2607, E-ISSN 1872-7565, Vol. 189, article id 105309Article in journal (Refereed)
    Abstract [en]

    Aim: To construct a Treatment Response Index from Multiple Sensors (TRIMS) for quantification of motor state in patients with Parkinson's disease (PD) during a single levodopa dose. Another aim was to compare TRIMS to sensor indexes derived from individual motor tasks.

    Method: Nineteen PD patients performed three motor tests including leg agility, pronation-supination movement of hands, and walking in a clinic while wearing inertial measurement unit sensors on their wrists and ankles. They performed the tests repeatedly before and after taking 150% of their individual oral levodopa-carbidopa equivalent morning dose.Three neurologists blinded to treatment status, viewed patients’ videos and rated their motor symptoms, dyskinesia, overall motor state based on selected items of Unified PD Rating Scale (UPDRS) part III, Dyskinesia scale, and Treatment Response Scale (TRS). To build TRIMS, out of initially 178 extracted features from upper- and lower-limbs data, 39 features were selected by stepwise regression method and were used as input to support vector machines to be mapped to mean reference TRS scores using 10-fold cross-validation method. Test-retest reliability, responsiveness to medication, and correlation to TRS as well as other UPDRS items were evaluated for TRIMS.

    Results: The correlation of TRIMS with TRS was 0.93. TRIMS had good test-retest reliability (ICC=0.83). Responsiveness of the TRIMS to medication was good compared to TRS indicating its power in capturing the treatment effects. TRIMS was highly correlated to dyskinesia (R = 0.85), bradykinesia (R=0.84) and gait (R=0.79) UPDRS items. Correlation of sensor index from the upper-limb to TRS was 0.89.

    Conclusion: Using the fusion of upper- and lower-limbs sensor data to construct TRIMS provided accurate PD motor states estimation and responsive to treatment. In addition, quantification of upper-limb sensor data during walking test provided strong results.

  • 29.
    Aghazadeh, David
    Örebro University, School of Science and Technology.
    Utvärdering av tidsplaneringsverktyg för universitet2021Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Scheduling is most often a problem that occurs at schools and universities. Scheduling is a task where all activities must be assigned to time and space with the right resources. When scheduling is done manually, the task takes a long time and is difficult to solve. The time it takes to solve the task can take a large amount of time. With the help of the right tools, time can be shortened, a better schedule can be created, and the workforce can be placed elsewhere. Scheduling at Örebro University is done manually by the administration. The purpose of the project is to generate a ground for making decisions about tools that can be facilitating. The project's focus is on the timetabling problem for universities, not for schools or production planning. During the project, scheduling tools have been found and selected, with the requirement that they have open source code. For evaluation, I set out criteria I applied as the basis for comparison. The experiment design consisted of creating three different sample data to experiment on the tools. The purpose of the three different test data was to produce an assessment according to the points of the criteria. The tools that were most convincing were those that got the best results according to the assessment criteria.

  • 30.
    Agrawal, Vikas
    et al.
    IBM Research, , India.
    Archibald, Christopher
    Mississippi State University, Starkville, United States.
    Bhatt, Mehul
    University of Bremen, Bremen, Germany.
    Bui, Hung Hai
    Laboratory for Natural Language Understanding, Sunnyvale CA, United States.
    Cook, Diane J.
    Washington State University, Pullman WA, United States.
    Cortés, Juan
    University of Toulouse, Toulouse, France.
    Geib, Christopher W.
    Drexel University, Philadelphia PA, United States.
    Gogate, Vibhav
    Department of Computer Science, University of Texas, Dallas, United States.
    Guesgen, Hans W.
    Massey University, Palmerston North, New Zealand.
    Jannach, Dietmar
    Technical university Dortmund, Dortmund, Germany.
    Johanson, Michael
    University of Alberta, Edmonton, Canada.
    Kersting, Kristian
    Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme (IAIS), Sankt Augustin, Germany; The University of Bonn, Bonn, Germany.
    Konidaris, George
    Massachusetts Institute of Technology (MIT), Cambridge MA, United States.
    Kotthoff, Lars
    INSIGHT Centre for Data Analytics, University College Cork, Cork, Ireland.
    Michalowski, Martin
    Adventium Labs, Minneapolis MN, United States.
    Natarajan, Sriraam
    Indiana University, Bloomington IN, United States.
    O’Sullivan, Barry
    INSIGHT Centre for Data Analytics, University College Cork, Cork, Ireland.
    Pickett, Marc
    Naval Research Laboratory, Washington DC, United States.
    Podobnik, Vedran
    Telecommunication Department of the Faculty of Electrical Engineering and Computing, University of University of Zagreb, Zagreb, Croatia.
    Poole, David
    Department of Computer Science, University of British Columbia, Vancouver, Canada.
    Shastri, Lokendra
    Infosys, , India.
    Shehu, Amarda
    George Mason University, Washington, United States.
    Sukthankar, Gita
    University of Central Florida, Orlando FL, United States.
    The AAAI-13 Conference Workshops2013In: The AI Magazine, ISSN 0738-4602, Vol. 34, no 4, p. 108-115Article in journal (Refereed)
    Abstract [en]

    The AAAI-13 Workshop Program, a part of the 27th AAAI Conference on Artificial Intelligence, was held Sunday and Monday, July 14-15, 2013, at the Hyatt Regency Bellevue Hotel in Bellevue, Washington, USA. The program included 12 workshops covering a wide range of topics in artificial intelligence, including Activity Context-Aware System Architectures (WS-13-05); Artificial Intelligence and Robotics Methods in Computational Biology (WS-13-06); Combining Constraint Solving with Mining and Learning (WS-13-07); Computer Poker and Imperfect Information (WS-13-08); Expanding the Boundaries of Health Informatics Using Artificial Intelligence (WS-13-09); Intelligent Robotic Systems (WS-13-10); Intelligent Techniques for Web Personalization and Recommendation (WS-13-11); Learning Rich Representations from Low-Level Sensors (WS-13-12); Plan, Activity,, and Intent Recognition (WS-13-13); Space, Time, and Ambient Intelligence (WS-13-14); Trading Agent Design and Analysis (WS-13-15); and Statistical Relational Artificial Intelligence (WS-13-16)

  • 31.
    Agélii Genlott, Annika
    et al.
    Örebro University, Örebro University School of Business.
    Grönlund, Åke
    Örebro University, Örebro University School of Business.
    Closing the gaps: Improving literacy and mathematics by ict-enhanced collaboration2016In: Computers and education, ISSN 0360-1315, E-ISSN 1873-782X, Vol. 99, p. 68-80Article in journal (Refereed)
    Abstract [en]

    Literacy and mathematics are necessary skills that for different reasons unfortunately not everybody acquires sufficiently. In OECD countries there is also a gender gap; boys lag behind girls in literacy but often outperform girls in mathematics (OECD, 2012). ICT (Information and communication technologies) may contribute useful tools to address both these problems but in order to effectively create better educational conditions there is yet a need to develop effective methods that combine ICT with key factors for learning. This research contributes to this by measuring effects of the “Write to Learn” (WTL) method. WTL lets children from 1st grade use several ICT tools to write texts and subsequently discuss and refine them together with classmates and teachers using digital real-time formative feedback and assessment. The central learning factor addressed, in mathematics as well as in literacy, is the written communication allowing the learners to interact with peers and teachers. WTL draws on methods from socio-cultural theory, including continuous social interaction and written real-time formative feedback among peers, using shared electronic forums for collaboration, thereby providing social meaning and increased learning of literacy and mathematics, among both boys and girls.

    The study uses quantitative methods and two control groups, one using traditional method (no ICT) and one using technology individually (without integrated social interaction and formative feedback), to compare results from 502 students in grade 3 national tests in mathematics and literacy. WTL yields by far best results; higher average score both in literacy and mathematics, smaller gender gap, and significantly better results for the under-achievers. The ITU method performs worst, which shows that ICT use must be well integrated into the pedagogy to be useful.

  • 32.
    Ahlberg, Ernst
    et al.
    Predictive Compound ADME & Safety, Drug Safety & Metabolism, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Winiwarter, Susanne
    Predictive Compound ADME & Safety, Drug Safety & Metabolism, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Boström, Henrik
    Department of Computer and Systems Sciences, Stockholm University, Sweden.
    Linusson, Henrik
    Department of Information Technology, University of Borås, Sweden.
    Löfström, Tuve
    Högskolan i Jönköping, JTH. Forskningsmiljö Datavetenskap och informatik, Jönköping, Sweden.
    Norinder, Ulf
    Swetox, Karolinska Institutet, Unit of Toxicology Sciences, Stockholm, Sweden.
    Johansson, Ulf
    Högskolan i Jönköping, JTH, Datateknik och informatik, Jönköping, Sweden.
    Engkvist, Ola
    External Sciences, Discovery Sciences, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Hammar, Oscar
    Quantitative Biology, Discovery Sciences, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Bendtsen, Claus
    Quantitative Biology, Discovery Sciences, AstraZeneca IMED Biotech Unit, Cambridge, England.
    Carlsson, Lars
    Quantitative Biology, Discovery Sciences, AstraZeneca IMED Biotech Unit, Mölndal, Sweden.
    Using conformal prediction to prioritize compound synthesis in drug discovery2017In: Proceedings of Machine Learning Research: Volume 60: Conformal and Probabilistic Prediction and Applications, 13-16 June 2017, Stockholm, Sweden / [ed] Alex Gammerman, Vladimir Vovk, Zhiyuan Luo, and Harris Papadopoulos, Stockholm, 2017, p. 174-184Conference paper (Refereed)
    Abstract [en]

    The choice of how much money and resources to spend to understand certain problems is of high interest in many areas. This work illustrates how computational models can be more tightly coupled with experiments to generate decision data at lower cost without reducing the quality of the decision. Several different strategies are explored to illustrate the trade off between lowering costs and quality in decisions.

    AUC is used as a performance metric and the number of objects that can be learnt from is constrained. Some of the strategies described reach AUC values over 0.9 and outperforms strategies that are more random. The strategies that use conformal predictor p-values show varying results, although some are top performing.

    The application studied is taken from the drug discovery process. In the early stages of this process compounds, that potentially could become marketed drugs, are being routinely tested in experimental assays to understand the distribution and interactions in humans.

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  • 33.
    Ahlberg, Filip
    et al.
    Örebro University, Örebro University School of Business.
    Öberg, Erik
    Örebro University, Örebro University School of Business.
    Hur demografiska faktorer påverkarförståelsen av teknisk skuld; en kvantitativjämförelse.2023Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
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  • 34.
    Ahlinder, Mikael
    et al.
    Örebro University, Swedish Business School at Örebro University.
    Wiklund, Martin
    Örebro University, Swedish Business School at Örebro University.
    ANVÄNDBARHET OCH HANDLINGSBARHET PÅ ELEKTRONISKA B2B MARKNADSPLATSER: En fallstudie på Visma Proceedo ur leverantörens perspektiv2009Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [sv]

    De elektroniska marknadsplatsernas betydelse för affärstransaktioner världen över har ökat dramatiskt de senaste åren. Allt fler företag väljer att ta med sina befintliga kundkontakter eller skapa nya via dessa marknadsplatser. Dock finns det vissa problem med dessa, ofta webbaserade system, de har inte en tillfred- ställande grad av användbarhet respektive handlingsbarhet. Syftet med vår undersökning var att beskriva vilka aspekter av de olika perspek- tiven, användbarhet samt handlingsbarhet, som kan anses vara mer betydande än andra att ta hänsyn till vid utveckling av elektroniska B2B marknadsplatser. Användbarhet är ett perspektiv som behandlar aspekter som att systemet skall vara lätt att använda, lätt att lära, subjektivt tilltalande osv. Handlingsbarhet som perspektiv tar till skillnad från användbarheten en mer kommunikativ utgångs- punkt, där systemets förmåga att fungera som en kommunikativ länk i de verk- samhetshandlingar som utförs sätts i fokus. Vi har genomfört en kvalitativ fallstudie på marknadsplatsoperatören Visma Proceedo’s webbsystem Supplier Center. Datainsamling har främst skett genom telefonintervjuer med tre olika företag som använder sig av Supplier Center som lösning för deras kundkommunikation.  Vi har i våra slutsatser kommit fram till att vissa aspekter av de två perspektiven är viktigare att beakta vi utformning av ett system likt Supplier Center. Ett så- dant system skall tillgodose kraven på tydlig handlingsrepertoar, handlingstrans- parent, tydlig feedback, personifiering, känd och begriplig vokabulär, intentionellt tydligt, handlingsstödjande, minimera användarens minnesbelastning, enhetlighet, förse användaren med återkoppling, förse användaren med klart markerade funk- tioner för att avbryta dialogen, bra felmeddelanden och förhindra fel.

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  • 35.
    Ahlqvist, Fabian
    et al.
    Örebro University, Örebro University School of Business.
    Lagerstedt Ekholm, Viggo
    Örebro University, Örebro University School of Business.
    Det ska vara lätt att göra rätt - En uppsats om hälso- och sjukvårdsorganisationers arbete med informationssäkerhetskultur för att motverka icke-illvilliga informationssäkerhetsincidenter2022Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
  • 36.
    Ahmed, Farouq
    Örebro University, School of Science and Technology.
    Utökad automatisering av e-handel med shopify API2018Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
    Abstract [en]

    Competition among sellers is rapidly increasing within e-commerce due to the decreasing barrier of entry. In order for a seller to be relevant, effectiveness is a requirement that can be achieved by using different methods and tools that result in a successful and profitable online store. One of the methods that sellers use is drop shipping which allows sale of goods without inventory holding. This is done by ordering the products from a third part that ships to the customer the moment an order is placed, instead of purchasing the products in advance. This leads to cheaper and more effective trade for new and already existing sellers.Despite the good opportunities that drop shipping allows, it is a time consuming task that requires a high level of accuracy due to the many repetitive tasks that are performed during the sales process. This is a problem that can hinder sellers from using the drop shipping model. In order to make it easier for sellers to utilize this method, a program can perform the manual tasks automatically.In this report, different software architectural patterns are studied to build the base of a prototype program that could perform the manual tasks with a satisfactory level of automation.

  • 37.
    Ahmed, Mobyen Uddin
    et al.
    Örebro University, School of Science and Technology.
    Banaee, Hadi
    Örebro University, School of Science and Technology.
    Rafael-Palou, Xavier
    Barcelona Digital Technology Centre, Barcelona, Spain.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Intelligent Healthcare Services to Support Health Monitoring of Elderly2015In: INTERNET OF THINGS: USER-CENTRIC IOT, PT I, Springer, 2015, Vol. 150, p. 178-186Conference paper (Refereed)
    Abstract [en]

    This paper proposed an approach of intelligent healthcare services to support health monitoring of old people through the project named SAAPHO. Here, definition and architecture of the proposed healthcare services are presented considering six different health parameters such as: 1) physical activity, 2) blood pressure, 3) glucose, 4) medication compliance, 5) pulse monitoring and 6) weight monitoring. The outcome of the proposed services is evaluated in a case study where total 201 subjects from Spain and Slovenia are involved for user requirements analysis considering 1) end users, 2) clinicians, and 3) field study analysis perspectives. The result shows the potentiality and competence of the proposed healthcare services for the users.

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  • 38.
    Ahmed, Mobyen Uddin
    et al.
    Mälardalen University, Västerås, Sweden.
    Fotouhi, Hossein
    Mälardalen University, Västerås, Sweden.
    Köckemann, Uwe
    Örebro University, School of Science and Technology.
    Lindén, Maria
    Mälardalen University, Västerås, Sweden.
    Tomasic, Ivan
    Mälardalen University, Västerås, Sweden.
    Tsiftes, Nicolas
    RISE SICS, Stockholm, Sweden.
    Voigt, Thiemo
    RISE SICS, Stockholm, Sweden.
    Run-Time Assurance for the E-care@home System2018In: Internet of Things (IoT) Technologies for HealthCare (HealthyIoT 2017) / [ed] Ahmed, MU; Begum, S; Fasquel, JB, Springer, 2018, Vol. 225, p. 107-110Conference paper (Refereed)
    Abstract [en]

    This paper presents the design and implementation of the software for a run-time assurance infrastructure in the E-care home system. An experimental evaluation is conducted to verify that the run-time assurance infrastructure is functioning correctly, and to enable detecting performance degradation in experimental IoT network deployments within the context of E-care home.

  • 39.
    Ahmed, Mobyen Uddin
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Physical Activity Classification for Elderly based on Pulse Rate2013Conference paper (Refereed)
    Abstract [en]

    Physical activity is one of the key components for elderly in order to be actively ageing. However, it is difficult to differentiate and identify the body movement and actual physical activity using only accelerometer measurement. Therefore, this paper presents an application of case-based retrieval classification scheme to classify the physical activity of elderly based on pulse rate measurements. Here, case-based retrieval approach used the features extracted from both time and frequency domain. The evaluation result shows the best accuracy performance while considering the combination of time and frequency domain features. According to the evaluation result while considering the control measurements, the sensitivity, specificity and overall accuracy are achieved as 95%, 96% and 96% respectively. Considering the test dataset, the system was succeeded to identify 13 physical activities out of 16 i.e. the percentage of the correctness was 81%.

  • 40.
    Ahmed, Mobyen Uddin
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Physical activity identification using supervised machine learning and based on pulse rate2013In: International Journal of Advanced Computer Sciences and Applications, ISSN 2158-107X, E-ISSN 2156-5570, Vol. 4, no 7, p. 210-217Article in journal (Refereed)
    Abstract [en]

    Physical activity is one of the key components for elderly in order to be actively ageing. Pulse rate is a convenient physiological parameter to identify elderly’s physical activity since it increases with activity and decreases with rest. However, analysis and classification of pulse rate is often difficult due to personal variation during activity. This paper proposed a Case-Based Reasoning (CBR) approach to identify physical activity of elderly based on pulse rate. The proposed CBR approach has been compared with the two popular classification techniques, i.e. Support Vector Machine (SVM) and Neural Network (NN). The comparison has been conducted through an empirical experimental study where three experiments with 192 pulse rate measurement data are used. The experiment result shows that the proposed CBR approach outperforms the other two methods. Finally, the CBR approach identifies physical activity of elderly 84% accurately based on pulse rate

    Download full text (pdf)
    Physical activity identification using supervised machine learning and based on pulse rate
  • 41.
    Ahmed, Rehan M.
    et al.
    Örebro University, School of Science and Technology.
    Ananiev, Anani V.
    Örebro University, School of Science and Technology.
    Kalaykov, Ivan
    Örebro University, School of Science and Technology.
    Compliant motion control for safe human robot interaction2009In: Robot motion and control 2009 / [ed] Krzysztof R. Kozłowski, Berlin: Springer , 2009, p. 265-274Conference paper (Refereed)
    Abstract [en]

    Robots have recently been foreseen to work side by side and share workspace with humans in assisting them in tasks that include physical human-robot (HR) interaction. The physical contact with human tasks under uncertainty has to be performed in a stable and safe manner [6]. However, current industrial robot manipulators are still very far from HR coexisting environments, because of their unreliable safety, rigidity and heavy structure. Besides this, the industrial norms separate the two spaces occupied by a human and a robot by means of physical fence or wall [9]. Therefore, the success of such physical HR interaction is possible if the robot is enabled to handle this interaction in a smart way to prevent injuries and damages.

  • 42.
    Ahmed, Rehan M.
    et al.
    Örebro University, School of Science and Technology.
    Kalaykov, Ivan
    Örebro University, School of Science and Technology.
    Ananiev, Anani
    Örebro University, School of Science and Technology.
    Modeling of magneto rheological fluid actuator enabling safe human-robot interaction2008In: IEEE International Conference on Emerging Technologies and Factory Automation, 2008. ETFA 2008, 2008, p. 974-979Conference paper (Refereed)
    Abstract [en]

    Impedance control and compliant behavior for safe human-robot physical interaction of industrial robots normally can be achieved by using active compliance control of actuators based on various sensor data. Alternatively, passive devices allow controllable compliance motion but usually are mechanically complex. We present another approach using a novel actuation mechanism based on magneto-rheological fluid (MRF) that incorporates variable stiffness directly into the joints. In this paper, we have investigated and analyzed principle characteristics of MRF actuation mechanism and presented the analytical-model. Then we have developed the static and dynamic model based on experimental test results and have discussed three essential modes of motion needed for human-robot manipulation interactive tasks.

  • 43.
    Ahnaf, S.M. Azoad
    et al.
    Computational Color and Spectral Image Analysis Lab, Computer Science and Engineering, Discipline Khulna University, Khulna, Bangladesh.
    Rahaman, G. M. Atiqur
    Computational Color and Spectral Image Analysis Lab, Computer Science and Engineering, Discipline Khulna University, Khulna, Bangladesh.
    Saha, Sajib
    Australian e-health Research Centre, CSIRO, Perth, Australia.
    Understanding CNN's Decision Making on OCT-based AMD Detection2021In: 2021 International Conference on Electronics, Communications and Information Technology (ICECIT), 14-16 Sept. 2021, IEEE, 2021, p. 1-4Conference paper (Refereed)
    Abstract [en]

    Age-related Macular degeneration (AMD) is the third leading cause of incurable acute central vision loss. Optical coherence tomography (OCT) is a diagnostic process used for both AMD and diabetic macular edema (DME) detection. Spectral-domain OCT (SD-OCT), an improvement of traditional OCT, has revolutionized assessing AMD for its high acquiring rate, high efficiency, and resolution. To detect AMD from normal OCT scans many techniques have been adopted. Automatic detection of AMD has become popular recently. The use of a deep Convolutional Neural Network (CNN) has helped its cause vastly. Despite having achieved better performance, CNN models are often criticized for not giving any justification in decision-making. In this paper, we aim to visualize and critically analyze the decision of CNNs in context-based AMD detection. Multiple experiments were done using the DUKE OCT dataset, utilizing transfer learning in Resnet50 and Vgg16 model. After training the model for AMD detection, Gradient-weighted Class Activation Mapping (Grad-Cam) is used for feature visualization. With the feature mapped image, each layer mask was compared. We have found out that the Outer Nuclear layer to the Inner segment myeloid (ONL-ISM) has more predominance about 17.13% for normal and 6.64% for AMD in decision making.

  • 44.
    Ahtiainen, Juhana
    et al.
    Department of Electrical Engineering and Automation, Aalto University, Espoo, Finland.
    Stoyanov, Todor
    Örebro University, School of Science and Technology.
    Saarinen, Jari
    GIM Ltd., Espoo, Finland.
    Normal Distributions Transform Traversability Maps: LIDAR-Only Approach for Traversability Mapping in Outdoor Environments2017In: Journal of Field Robotics, ISSN 1556-4959, E-ISSN 1556-4967, Vol. 34, no 3, p. 600-621Article in journal (Refereed)
    Abstract [en]

    Safe and reliable autonomous navigation in unstructured environments remains a challenge for field robots. In particular, operating on vegetated terrain is problematic, because simple purely geometric traversability analysis methods typically classify dense foliage as nontraversable. As traversing through vegetated terrain is often possible and even preferable in some cases (e.g., to avoid executing longer paths), more complex multimodal traversability analysis methods are necessary. In this article, we propose a three-dimensional (3D) traversability mapping algorithm for outdoor environments, able to classify sparsely vegetated areas as traversable, without compromising accuracy on other terrain types. The proposed normal distributions transform traversability mapping (NDT-TM) representation exploits 3D LIDAR sensor data to incrementally expand normal distributions transform occupancy (NDT-OM) maps. In addition to geometrical information, we propose to augment the NDT-OM representation with statistical data of the permeability and reflectivity of each cell. Using these additional features, we train a support-vector machine classifier to discriminate between traversable and nondrivable areas of the NDT-TM maps. We evaluate classifier performance on a set of challenging outdoor environments and note improvements over previous purely geometrical traversability analysis approaches.

  • 45.
    Akalin, Neziha
    Örebro University, School of Science and Technology.
    Perceived Safety in Social Human-Robot Interaction2022Doctoral thesis, comprehensive summary (Other academic)
    Abstract [en]

    This compilation thesis contributes to a deeper understanding of perceived safety in human-robot interaction (HRI) with a particular focus on social robots. The current understanding of safety in HRI is mostly limited to physical safety, whereas perceived safety has often been neglected and underestimated. However, safe HRI requires a conceptualization of safety that goes beyond physical safety covering also perceived safety of the users. Within this context, this thesis provides a comprehensive analysis of perceived safety in HRI with social robots, considering a diverse set of human-related and robot-related factors.

    Two particular challenges for providing perceived safety in HRI are 1) understanding and evaluating human safety perception through direct and indirect measures, and 2) utilizing the measured level of perceived safety for adapting the robot behaviors. The primary contribution of this dissertation is in addressing the first challenge. The thesis investigates perceived safety in HRI by alternating between conducting user studies, literature review, and testing the findings from the literature within user studies.

    In this thesis, six main factors influencing perceived safety in HRI are lifted: the context of robot use, the user’s comfort, experience and familiarity with robots, trust, sense of control over the interaction, and transparent and predictable robot behaviors. These factors could provide a common understanding of perceived safety and bridge the theoretical gap in the literature. Moreover, this thesis proposes an experimental paradigm to observe and quantify perceived safety using objective and subjective measures. This contributes to bridging the methodological gap in the literature.

    The six factors are reviewed in HRI literature, and the robot features that affect these factors are organized in a taxonomy. Although this taxonomy focuses on social robots, the identified characteristics are relevant to other types of robots and autonomous systems. In addition to the taxonomy, the thesis provides a set of guidelines for providing perceived safety in social HRI. As a secondary contribution, the thesis presents an overview of reinforcement learning applications in social robotics as a suitable learning mechanism for adapting the robots’ behaviors to mitigate psychological harm.

    List of papers
    1. An Evaluation Tool of the Effect of Robots in Eldercare on the Sense of Safety and Security
    Open this publication in new window or tab >>An Evaluation Tool of the Effect of Robots in Eldercare on the Sense of Safety and Security
    2017 (English)In: Social Robotics: 9th International Conference, ICSR 2017, Tsukuba, Japan, November 22-24, 2017, Proceedings / [ed] Kheddar, A.; Yoshida, E.; Ge, S.S.; Suzuki, K.; Cabibihan, J-J:, Eyssel, F:, He, H., Springer International Publishing , 2017, p. 628-637Conference paper, Published paper (Refereed)
    Abstract [en]

    The aim of the study presented in this paper is to develop a quantitative evaluation tool of the sense of safety and security for robots in eldercare. By investigating the literature on measurement of safety and security in human-robot interaction, we propose new evaluation tools. These tools are semantic differential scale questionnaires. In experimental validation, we used the Pepper robot, programmed in the way to exhibit social behaviors, and constructed four experimental conditions varying the degree of the robot’s non-verbal behaviors from no gestures at all to full head and hand movements. The experimental results suggest that both questionnaires (for the sense of safety and the sense of security) have good internal consistency.

    Place, publisher, year, edition, pages
    Springer International Publishing, 2017
    Series
    Lecture Notes in Computer Science, ISSN 0302-9743, E-ISSN 1611-3349 ; 10652
    Keywords
    Sense of safety, Sense of security, Eldercare, Video-based evaluation, Quantitative evaluation tool
    National Category
    Computer Systems Computer Vision and Robotics (Autonomous Systems)
    Research subject
    Computer Science
    Identifiers
    urn:nbn:se:oru:diva-62768 (URN)10.1007/978-3-319-70022-9_62 (DOI)000449941100062 ()2-s2.0-85035814295 (Scopus ID)978-3-319-70022-9 (ISBN)978-3-319-70021-2 (ISBN)
    Conference
    9th International Conference on Social Robotics (ICSR 2017), Tsukuba, Japan, November 22-24, 2017
    Projects
    SOCRATES
    Funder
    EU, Horizon 2020, 721619
    Available from: 2017-11-22 Created: 2017-11-22 Last updated: 2024-01-16Bibliographically approved
    2. Evaluating the Sense of Safety and Security in Human - Robot Interaction with Older People
    Open this publication in new window or tab >>Evaluating the Sense of Safety and Security in Human - Robot Interaction with Older People
    2019 (English)In: Social Robots: Technological, Societal and Ethical Aspects of Human-Robot Interaction / [ed] Oliver Korn, Springer, 2019, p. 237-264Chapter in book (Refereed)
    Abstract [en]

    For many applications where interaction between robots and older people takes place, safety and security are key dimensions to consider. ‘Safety’ refers to a perceived threat of physical harm, whereas ‘security’ is a broad term which refers to many aspects related to health, well-being, and aging. This chapter presents a quantitative evaluation tool of the sense of safety and security for robots in elder care. By investigating the literature on measurement of safety and security in human–robot interaction, we propose new evaluation tools specially tailored to assess interaction between robots and older people.

    Place, publisher, year, edition, pages
    Springer, 2019
    Series
    Human-Computer Interaction Series, ISSN 1571-5035, E-ISSN 2524-4477
    Keywords
    Sense of safety and security, Quantitative evaluation tool, Social robots, Elder care
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:oru:diva-78493 (URN)10.1007/978-3-030-17107-0_12 (DOI)978-3-030-17106-3 (ISBN)978-3-030-17107-0 (ISBN)
    Available from: 2019-12-08 Created: 2019-12-08 Last updated: 2024-01-16Bibliographically approved
    3. The Influence of Feedback Type in Robot-Assisted Training
    Open this publication in new window or tab >>The Influence of Feedback Type in Robot-Assisted Training
    2019 (English)In: Multimodal Technologies and Interaction, E-ISSN 2414-4088, Vol. 3, no 4Article in journal (Refereed) Published
    Abstract [en]

    Robot-assisted training, where social robots can be used as motivational coaches, provides an interesting application area. This paper examines how feedback given by a robot agent influences the various facets of participant experience in robot-assisted training. Specifically, we investigated the effects of feedback type on robot acceptance, sense of safety and security, attitude towards robots and task performance. In the experiment, 23 older participants performed basic arm exercises with a social robot as a guide and received feedback. Different feedback conditions were administered, such as flattering, positive and negative feedback. Our results suggest that the robot with flattering and positive feedback was appreciated by older people in general, even if the feedback did not necessarily correspond to objective measures such as performance. Participants in these groups felt better about the interaction and the robot.

    Place, publisher, year, edition, pages
    Multidisciplinary Digital Publishing Institute, 2019
    Keywords
    feedback, acceptance, flattering robot, sense of safety and security, robot-assisted training
    National Category
    Computer Vision and Robotics (Autonomous Systems)
    Identifiers
    urn:nbn:se:oru:diva-78492 (URN)10.3390/mti3040067 (DOI)000623570700003 ()2-s2.0-85079720466 (Scopus ID)
    Funder
    EU, Horizon 2020, 721619
    Available from: 2019-12-08 Created: 2019-12-08 Last updated: 2024-01-16Bibliographically approved
    4. Do you feel safe with your robot? Factors influencing perceived safety in human-robot interaction based on subjective and objective measures
    Open this publication in new window or tab >>Do you feel safe with your robot? Factors influencing perceived safety in human-robot interaction based on subjective and objective measures
    2022 (English)In: International journal of human-computer studies, ISSN 1071-5819, E-ISSN 1095-9300, Vol. 158, article id 102744Article in journal (Refereed) Published
    Abstract [en]

    Safety in human-robot interaction can be divided into physical safety and perceived safety, where the later is still under-addressed in the literature. Investigating perceived safety in human-robot interaction requires a multidisciplinary perspective. Indeed, perceived safety is often considered as being associated with several common factors studied in other disciplines, i.e., comfort, predictability, sense of control, and trust. In this paper, we investigated the relationship between these factors and perceived safety in human-robot interaction using subjective and objective measures. We conducted a two-by-five mixed-subjects design experiment. There were two between-subjects conditions: the faulty robot was experienced at the beginning or the end of the interaction. The five within-subjects conditions correspond to (1) baseline, and the manipulations of robot behaviors to stimulate: (2) discomfort, (3) decreased perceived safety, (4) decreased sense of control and (5) distrust. The idea of triggering a deprivation of these factors was motivated by the definition of safety in the literature where safety is often defined by the absence of it. Twenty-seven young adult participants took part in the experiments. Participants were asked to answer questionnaires that measure the manipulated factors after within-subjects conditions. Besides questionnaire data, we collected objective measures such as videos and physiological data. The questionnaire results show a correlation between comfort, sense of control, trust, and perceived safety. Since these factors are the main factors that influence perceived safety, they should be considered in human-robot interaction design decisions. We also discuss the effect of individual human characteristics (such as personality and gender) that they could be predictors of perceived safety. We used the physiological signal data and facial affect from videos for estimating perceived safety where participants’ subjective ratings were utilized as labels. The data from objective measures revealed that the prediction rate was higher from physiological signal data. This paper can play an important role in the goal of better understanding perceived safety in human-robot interaction.

    Place, publisher, year, edition, pages
    Academic Press, 2022
    Keywords
    Perceived safety, Human robot interaction, Comfort, Sense of control, Trust, Physiological signal data, Facial expressions, Multidisciplinary perspective
    National Category
    Robotics
    Research subject
    Human-Computer Interaction; Computer Science
    Identifiers
    urn:nbn:se:oru:diva-95673 (URN)10.1016/j.ijhcs.2021.102744 (DOI)000782270600008 ()2-s2.0-85119702541 (Scopus ID)
    Available from: 2021-11-29 Created: 2021-11-29 Last updated: 2024-01-16Bibliographically approved
    5. Reinforcement Learning Approaches in Social Robotics
    Open this publication in new window or tab >>Reinforcement Learning Approaches in Social Robotics
    2021 (English)In: Sensors, E-ISSN 1424-8220, Vol. 21, no 4, article id 1292Article, review/survey (Refereed) Published
    Abstract [en]

    This article surveys reinforcement learning approaches in social robotics. Reinforcement learning is a framework for decision-making problems in which an agent interacts through trial-and-error with its environment to discover an optimal behavior. Since interaction is a key component in both reinforcement learning and social robotics, it can be a well-suited approach for real-world interactions with physically embodied social robots. The scope of the paper is focused particularly on studies that include social physical robots and real-world human-robot interactions with users. We present a thorough analysis of reinforcement learning approaches in social robotics. In addition to a survey, we categorize existent reinforcement learning approaches based on the used method and the design of the reward mechanisms. Moreover, since communication capability is a prominent feature of social robots, we discuss and group the papers based on the communication medium used for reward formulation. Considering the importance of designing the reward function, we also provide a categorization of the papers based on the nature of the reward. This categorization includes three major themes: interactive reinforcement learning, intrinsically motivated methods, and task performance-driven methods. The benefits and challenges of reinforcement learning in social robotics, evaluation methods of the papers regarding whether or not they use subjective and algorithmic measures, a discussion in the view of real-world reinforcement learning challenges and proposed solutions, the points that remain to be explored, including the approaches that have thus far received less attention is also given in the paper. Thus, this paper aims to become a starting point for researchers interested in using and applying reinforcement learning methods in this particular research field.

    Place, publisher, year, edition, pages
    MDPI, 2021
    Keywords
    Human-robot interaction, physical embodiment, reinforcement learning, reward design, social robotics
    National Category
    Robotics
    Identifiers
    urn:nbn:se:oru:diva-90245 (URN)10.3390/s21041292 (DOI)000624663200001 ()33670257 (PubMedID)2-s2.0-85100651693 (Scopus ID)
    Funder
    EU, Horizon 2020, 721619
    Available from: 2021-03-08 Created: 2021-03-08 Last updated: 2024-01-16Bibliographically approved
    6. Guidelines for Identifying Factors Influencing Perceived Safety in Human-Robot Interaction
    Open this publication in new window or tab >>Guidelines for Identifying Factors Influencing Perceived Safety in Human-Robot Interaction
    (English)Manuscript (preprint) (Other academic)
    National Category
    Computer Sciences
    Identifiers
    urn:nbn:se:oru:diva-98456 (URN)
    Available from: 2022-04-04 Created: 2022-04-04 Last updated: 2022-04-04Bibliographically approved
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  • 46.
    Akalin, Neziha
    et al.
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Kristoffersson, Annica
    School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    A Taxonomy of Factors Influencing Perceived Safety in Human-Robot Interaction2023In: International Journal of Social Robotics, ISSN 1875-4791, E-ISSN 1875-4805, Vol. 15, p. 1993-2004Article in journal (Refereed)
    Abstract [en]

    Safety is a fundamental prerequisite that must be addressed before any interaction of robots with humans. Safety has been generally understood and studied as the physical safety of robots in human-robot interaction, whereas how humans perceive these robots has received less attention. Physical safety is a necessary condition for safe human-robot interaction. However, it is not a sufficient condition. A robot that is safe by hardware and software design can still be perceived as unsafe. This article focuses on perceived safety in human-robot interaction. We identified six factors that are closely related to perceived safety based on the literature and the insights obtained from our user studies. The identified factors are the context of robot use, comfort, experience and familiarity with robots, trust, the sense of control over the interaction, and transparent and predictable robot actions. We then made a literature review to identify the robot-related factors that influence perceived safety. Based the literature, we propose a taxonomy which includes human-related and robot-related factors. These factors can help researchers to quantify perceived safety of humans during their interactions with robots. The quantification of perceived safety can yield computational models that would allow mitigating psychological harm.

  • 47.
    Akalin, Neziha
    et al.
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Kristoffersson, Annica
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    An Evaluation Tool of the Effect of Robots in Eldercare on the Sense of Safety and Security2017In: Social Robotics: 9th International Conference, ICSR 2017, Tsukuba, Japan, November 22-24, 2017, Proceedings / [ed] Kheddar, A.; Yoshida, E.; Ge, S.S.; Suzuki, K.; Cabibihan, J-J:, Eyssel, F:, He, H., Springer International Publishing , 2017, p. 628-637Conference paper (Refereed)
    Abstract [en]

    The aim of the study presented in this paper is to develop a quantitative evaluation tool of the sense of safety and security for robots in eldercare. By investigating the literature on measurement of safety and security in human-robot interaction, we propose new evaluation tools. These tools are semantic differential scale questionnaires. In experimental validation, we used the Pepper robot, programmed in the way to exhibit social behaviors, and constructed four experimental conditions varying the degree of the robot’s non-verbal behaviors from no gestures at all to full head and hand movements. The experimental results suggest that both questionnaires (for the sense of safety and the sense of security) have good internal consistency.

    Download full text (pdf)
    An Evaluation Tool of the Effect of Robots in Eldercare on the Sense of Safety and Security
  • 48.
    Akalin, Neziha
    et al.
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Kristoffersson, Annica
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    The Relevance of Social Cues in Assistive Training with a Social Robot2018In: 10th International Conference on Social Robotics, ICSR 2018, Proceedings / [ed] Ge, S.S., Cabibihan, J.-J., Salichs, M.A., Broadbent, E., He, H., Wagner, A., Castro-González, Á., Springer, 2018, p. 462-471Conference paper (Refereed)
    Abstract [en]

    This paper examines whether social cues, such as facial expressions, can be used to adapt and tailor a robot-assisted training in order to maximize performance and comfort. Specifically, this paper serves as a basis in determining whether key facial signals, including emotions and facial actions, are common among participants during a physical and cognitive training scenario. In the experiment, participants performed basic arm exercises with a social robot as a guide. We extracted facial features from video recordings of participants and applied a recursive feature elimination algorithm to select a subset of discriminating facial features. These features are correlated with the performance of the user and the level of difficulty of the exercises. The long-term aim of this work, building upon the work presented here, is to develop an algorithm that can eventually be used in robot-assisted training to allow a robot to tailor a training program based on the physical capabilities as well as the social cues of the users.

    Download full text (pdf)
    The Relevance of Social Cues in Assistive Training with a Social Robot
  • 49.
    Akalin, Neziha
    et al.
    Örebro University, School of Science and Technology.
    Krakovsky, Maya
    Department of Industrial Engineering and Management, and ABC Robotics Initiative, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
    Avioz-Sarig, Omri
    Department of Industrial Engineering and Management, and ABC Robotics Initiative, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Edan, Yael
    Department of Industrial Engineering and Management, and ABC Robotics Initiative, Ben-Gurion University of the Negev, Beer-Sheva, Israel.
    Robot-Assisted Training with Swedish and Israeli Older Adults2021In: Social Robotics: 13th International Conference, ICSR 2021, Singapore, Singapore, November 10–13, 2021, Proceedings / [ed] Haizhou Li; Shuzhi Sam Ge; Yan Wu; Agnieszka Wykowska; Hongsheng He; Xiaorui Liu; Dongyu Li; Jairo Perez-Osorio, Springer, 2021, p. 487-496Conference paper (Refereed)
    Abstract [en]

    This paper explores robot-assisted training in a cross-cultural context with older adults. We performed user studies with 28 older adults with two different assistive training robots: an adaptive robot, and a non-adaptive robot, in two countries (Sweden and Israel). In the adaptive robot group, the robot suggested playing music and decreased the number of repetitions based on the participant’s level of engagement. We analyzed the facial expressions of the participants in these two groups. Results revealed that older adults in the adaptive robot group showed more varying facial expressions. The adaptive robot created a distraction for the older adults since it talked more than the non-adaptive robot. This result suggests that a robot designed for older adults should utilize the right amount of communication capabilities. The Israeli participants expressed more positive attitudes towards robots and rated the perceived usefulness of the robot higher than the Swedish participants.

  • 50.
    Akalin, Neziha
    et al.
    Örebro University, School of Science and Technology.
    Kristoffersson, Annica
    School of Innovation, Design and Engineering, Mälardalen University, Västerås, Sweden.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Guidelines for Identifying Factors Influencing Perceived Safety in Human-Robot InteractionManuscript (preprint) (Other academic)
1234567 1 - 50 of 2807
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