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  • 1.
    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, 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, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 10, 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.

  • 2.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    An Ontology-Based Reasoning Framework for Querying Satellite Images for Disaster Monitoring2017In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 11, 2545Article in journal (Refereed)
    Abstract [en]

    This paper presents a framework in which satellite images are classified and augmented with additional semantic information to enable queries about what can be found on the map at a particular location, but also about paths that can be taken. This is achieved by a reasoning framework based on qualitative spatial reasoning that is able to find answers to high level queries that may vary on the current situation. This framework called SemCityMap, provides the full pipeline from enriching the raw image data with rudimentary labels to the integration of a knowledge representation and reasoning methods to user interfaces for high level querying. To illustrate the utility of SemCityMap in a disaster scenario, we use an urban environment—central Stockholm—in combination with a flood simulation. We show that the system provides useful answers to high-level queries also with respect to the current flood status. Examples of such queries concern path planning for vehicles or retrieval of safe regions such as “find all regions close to schools and far from the flooded area”. The particular advantage of our approach lies in the fact that ontological information and reasoning is explicitly integrated so that queries can be formulated in a natural way using concepts on appropriate level of abstraction, including additional constraints.

  • 3.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Renoux, Jennifer
    Örebro University, School of Science and Technology.
    Köckemann, Uwe
    Örebro University, School of Science and Technology.
    Kristoffersson, Annica
    Örebro University, School of Science and Technology.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Blomqvist, Eva
    RISE SICS East, Linköping, Sweden.
    Tsiftes, Nicolas
    RISE SICS, Stockholm, Sweden.
    Voigt, Thiemo
    RISE SICS, Stockholm, Sweden.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    An Ontology-based Context-aware System for Smart Homes: E-care@home2017In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 7, 1586Article in journal (Refereed)
    Abstract [en]

    Smart home environments have a significant potential to provide for long-term monitoring of users with special needs in order to promote the possibility to age at home. Such environments are typically equipped with a number of heterogeneous sensors that monitor both health and environmental parameters. This paper presents a framework called E-care@home, consisting of an IoT infrastructure, which provides information with an unambiguous, shared meaning across IoT devices, end-users, relatives, health and care professionals and organizations. We focus on integrating measurements gathered from heterogeneous sources by using ontologies in order to enable semantic interpretation of events and context awareness. Activities are deduced using an incremental answer set solver for stream reasoning. The paper demonstrates the proposed framework using an instantiation of a smart environment that is able to perform context recognition based on the activities and the events occurring in the home.

  • 4.
    Arain, Muhammad Asif
    et al.
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Cirillo, Marcello
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Global coverage measurement planning strategies for mobile robots equipped with a remote gas sensor2015In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 15, no 3, 6845-6871 p.Article in journal (Refereed)
    Abstract [en]

    The problem of gas detection is relevant to many real-world applications, such as leak detection in industrial settings and landfill monitoring. In this paper, we address the problem of gas detection in large areas with a mobile robotic platform equipped with a remote gas sensor. We propose an algorithm that leverages a novel method based on convex relaxation for quickly solving sensor placement problems, and for generating an efficient exploration plan for the robot. To demonstrate the applicability of our method to real-world environments, we performed a large number of experimental trials, both on randomly generated maps and on the map of a real environment. Our approach proves to be highly efficient in terms of computational requirements and to provide nearly-optimal solutions.

  • 5.
    Banaee, Hadi
    et al.
    Örebro University, School of Science and Technology.
    Ahmed, Mobyen Uddin
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Data mining for wearable sensors in health monitoring systems: a review of recent trends and challenges2013In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 13, no 12, 17472-17500 p.Article in journal (Refereed)
    Abstract [en]

    The past few years have witnessed an increase in the development of wearable sensors for health monitoring systems. This increase has been due to several factors such as development in sensor technology as well as directed efforts on political and stakeholder levels to promote projects which address the need for providing new methods for care given increasing challenges with an aging population. An important aspect of study in such system is how the data is treated and processed. This paper provides a recent review of the latest methods and algorithms used to analyze data from wearable sensors used for physiological monitoring of vital signs in healthcare services. In particular, the paper outlines the more common data mining tasks that have been applied such as anomaly detection, prediction and decision making when considering in particular continuous time series measurements. Moreover, the paper further details the suitability of particular data mining and machine learning methods used to process the physiological data and provides an overview of the properties of the data sets used in experimental validation. Finally, based on this literature review, a number of key challenges have been outlined for data mining methods in health monitoring systems

  • 6.
    Di Lello, Enrico
    et al.
    Katholieke Univ Leuven, Div PMA, Dept Mech Engn, Heverlee, Belgium.
    Trincavelli, Marco
    Örebro University, School of Science and Technology. Centre for Applied Autonomous Sensor Systems, Örebro University, Örebro, Sweden;.
    Bruyninckx, Herman
    Katholieke Univ Leuven, Div PMA, Dept Mech Engn, Heverlee, Belgium; Eindhoven Univ Technol, Sect CST, Dept Mech Engn, Eindhoven, Netherlands .
    De laet, Tinne
    Katholieke Univ Leuven, Fac Engn Sci, Heverlee, Belgium.
    Augmented Switching Linear Dynamical System Model for Gas Concentration Estimation with MOX Sensors in an Open Sampling System2014In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, no 7, 12533-12559 p.Article in journal (Refereed)
    Abstract [en]

    In this paper, we introduce a Bayesian time series model approach for gas concentration estimation using Metal Oxide (MOX) sensors in Open Sampling System (OSS). Our approach focuses on the compensation of the slow response of MOX sensors, while concurrently solving the problem of estimating the gas concentration in OSS. The proposed Augmented Switching Linear System model allows to include all the sources of uncertainty arising at each step of the problem in a single coherent probabilistic formulation. In particular, the problem of detecting on-line the current sensor dynamical regime and estimating the underlying gas concentration under environmental disturbances and noisy measurements is formulated and solved as a statistical inference problem. Our model improves, with respect to the state of the art, where system modeling approaches have been already introduced, but only provided an indirect relative measures proportional to the gas concentration and the problem of modeling uncertainty was ignored. Our approach is validated experimentally and the performances in terms of speed of and quality of the gas concentration estimation are compared with the ones obtained using a photo-ionization detector.

  • 7.
    Fonollosa, Jordi
    et al.
    BioCircuits Institute, University of California San Diego, La Jolla, USA .
    Rodriguez-Lujan, Irene
    BioCircuits Institute, University of California San Diego, La Jolla, USA .
    Trincavelli, Marco
    Örebro University, School of Science and Technology. AASS Research Center, Örebro University, Örebro, Sweden .
    Vergara, Alexander
    Biomolecular Measurement Division, Material Measurement Laboratory, National Institute of Standards and Technology, Gaithersburg, USA.
    Huerta, Ramon
    BioCircuits Institute, University of California San Diego, La Jolla, USA.
    Chemical Discrimination in Turbulent Gas Mixtures with MOX Sensors Validated by Gas Chromatography-Mass Spectrometry2014In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, no 10, 19336-19353 p.Article in journal (Refereed)
    Abstract [en]

    Chemical detection systems based on chemo-resistive sensors usually include a gas chamber to control the sample air flow and to minimize turbulence. However, such a kind of experimental setup does not reproduce the gas concentration fluctuations observed in natural environments and destroys the spatio-temporal information contained in gas plumes. Aiming at reproducing more realistic environments, we utilize a wind tunnel with two independent gas sources that get naturally mixed along a turbulent flow. For the first time, chemo-resistive gas sensors are exposed to dynamic gas mixtures generated with several concentration levels at the sources. Moreover, the ground truth of gas concentrations at the sensor location was estimated by means of gas chromatography-mass spectrometry. We used a support vector machine as a tool to show that chemo-resistive transduction can be utilized to reliably identify chemical components in dynamic turbulent mixtures, as long as sufficient gas concentration coverage is used. We show that in open sampling systems, training the classifiers only on high concentrations of gases produces less effective classification and that it is important to calibrate the classification method with data at low gas concentrations to achieve optimal performance.

  • 8.
    Hernandez Bennetts, Victor
    et al.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Pomadera Sese, Victor
    Institute of Bioengineering of Catalonia, Barcelona, Spain.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Marco, Santiago
    Signal and Information Processing for Sensing Systema, Institute for Bioengineering of Catalonia, Barcelona, Spain; Departament d’Electrònica, Universitat de Barcelona, Barcelona, Spain.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Combining Non Selective Gas Sensors on a Mobile Robot for Identification and Mapping of Multiple Chemical Compounds2014In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, no 9, 17331-17352 p.Article in journal (Refereed)
    Abstract [en]

    In this paper, we address the task of gas distribution modeling in scenarios where multiple heterogeneous compounds are present. Gas distribution modeling is particularly useful in emission monitoring applications where spatial representations of the gaseous patches can be used to identify emission hot spots. In realistic environments, the presence of multiple chemicals is expected and therefore, gas discrimination has to be incorporated in the modeling process. The approach presented in this work addresses the task of gas distribution modeling by combining different non selective gas sensors. Gas discrimination is addressed with an open sampling system, composed by an array of metal oxide sensors and a probabilistic algorithm tailored to uncontrolled environments. For each of the identified compounds, the mapping algorithm generates a calibrated gas distribution model using the classification uncertainty and the concentration readings acquired with a photo ionization detector. The meta parameters of the proposed modeling algorithm are automatically learned from the data. The approach was validated with a gas sensitive robot patrolling outdoor and indoor scenarios, where two different chemicals were released simultaneously. The experimental results show that the generated multi compound maps can be used to accurately predict the location of emitting gas sources.

  • 9.
    Koshmak, Gregory
    et al.
    Mälardalen Univ, Sch Innovat Design & Engn, Västerås, Sweden.
    Linden, Maria
    Mälardalen Univ, Sch Innovat Design & Engn, Västerås, Sweden.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Dynamic Bayesian Networks for Context-Aware Fall Risk Assessment2014In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, no 5, 9330-9348 p.Article in journal (Refereed)
    Abstract [en]

    Fall incidents among the elderly often occur in the home and can cause serious injuries affecting their independent living. This paper presents an approach where data from wearable sensors integrated in a smart home environment is combined using a dynamic Bayesian network. The smart home environment provides contextual data, obtained from environmental sensors, and contributes to assessing a fall risk probability. The evaluation of the developed system is performed through simulation. Each time step is represented by a single user activity and interacts with a fall sensors located on a mobile device. A posterior probability is calculated for each recognized activity or contextual information. The output of the system provides a total risk assessment of falling given a response from the fall sensor.

  • 10. Kumar Mani, Ganesh
    et al.
    Sankar, Prabakaran
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Rayappan, John Bosco Balaguru
    Detection of spoiled meat using an electronic nose2014In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220Article in journal (Refereed)
  • 11.
    Lilienthal, Achim J.
    et al.
    Örebro University, Department of Technology.
    Loutfi, Amy
    Örebro University, Department of Technology.
    Duckett, Tom
    University of Lincoln.
    Airborne chemical sensing with mobile robots2006In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 6, no 11, 1616-1678 p.Article in journal (Refereed)
    Abstract [en]

    Airborne chemical sensing with mobile robots has been an active research area since the beginning of the 1990s. This article presents a review of research work in this field, including gas distribution mapping, trail guidance, and the different subtasks of gas source localisation. Due to the difficulty of modelling gas distribution in a real world environment with currently available simulation techniques, we focus largely on experimental work and do not consider publications that are purely based on simulations.

  • 12.
    Längkvist, Martin
    et al.
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Rayappan, John Bosco Balaguru
    SASTRA University, Thanjavur, India.
    Fast Classification of Meat Spoilage Markers Using Nanostructured ZnO Thin Films and Unsupervised Feature Learning2013In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 13, no 2, 1578-1592 p.Article in journal (Refereed)
    Abstract [en]

    This paper investigates a rapid and accurate detection system for spoilage in meat. We use unsupervised feature learning techniques (stacked restricted Boltzmann machines and auto-encoders) that consider only the transient response from undoped zinc oxide, manganese-doped zinc oxide, and fluorine-doped zinc oxide in order to classify three categories: the type of thin film that is used, the type of gas, and the approximate ppm-level of the gas. These models mainly offer the advantage that features are learned from data instead of being hand-designed. We compare our results to a feature-based approach using samples with various ppm level of ethanol and trimethylamine (TMA) that are good markers for meat spoilage. The result is that deep networks give a better and faster classification than the feature-based approach, and we thus conclude that the fine-tuning of our deep models are more efficient for this kind of multi-label classification task.

  • 13.
    Memedi, Mevludin
    et al.
    Örebro University, School of Science and Technology. School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Khan, Taha
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden; School of Innovation, Design and Technology, Mälardalen University, Västerås; Sweden.
    Grenholm, Peter
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, 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.
    Automatic and objective assessment of alternating tapping performance in parkinson’s disease2013In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 13, no 12, 16965-16984 p.Article in journal (Refereed)
    Abstract [en]

    This paper presents the development and evaluation of a method for enabling quantitative and automatic scoring of alternating tapping performance of patients with Parkinson‟s disease (PD). Ten healthy elderly subjects and 95 patients in different clinical stages of PD have utilized a touch-pad handheld computer to perform alternate tapping tests in their home environments. First, a neurologist used a web-based system to visually assess impairments in four tapping dimensions („speed‟, „accuracy‟, „fatigue‟ and „arrhythmia‟) and a global tapping severity (GTS). Second, tapping signals were processed with time series analysis and statistical methods to derive 24 quantitative parameters. Third, principal component analysis was used to reduce the dimensions of these parameters and to obtain scores for the four dimensions. Finally, a logistic regressionclassifier was trained using a 10-fold stratified cross-validation to map the reduced parameters to the corresponding visually assessed GTS scores. Results showed that the computed scores correlated well to visually assessed scores and were significantly different across Unified Parkinson‟s Disease Rating Scale scores of upper limb motor performance. In addition, they had good internal consistency, had good ability to discriminate between healthy elderly and patients in different disease stages, had good sensitivity to treatment interventions and could reflect the natural disease progression over time. In conclusion, the automatic method can be useful to objectively assess the tapping performance ofPD patients and can be included in telemedicine tools for remote monitoring of tapping.

  • 14.
    Memedi, Mevludin
    et al.
    School of Technology and Business Studies, Computer Engineering, Dalarna University, Falun, Sweden.
    Sadikov, Aleksander
    Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
    Groznik, Vida
    Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
    Zabkar, Jure
    Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
    Mozina, Martin
    Artificial Intelligence Laboratory, Faculty of Computer and Information Science, University of Ljubljana, Ljubljana, Slovenia.
    Bergquist, Filip
    Department of Pharmacology, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden.
    Johansson, Anders
    Department of Clinical Neuroscience, Neurology, Karolinska Institutet, Stockholm, Sweden.
    Haubenberger, Deitrich
    Clinical Trials Unit, Office of the Clinical Director, NINDS Intramural Research Program, National Institutes of Health, Bethesda MD, USA.
    Nyholm, Dag
    Department of Neuroscience, Neurology, Uppsala University, Uppsala, Sweden.
    Automatic Spiral Analysis for Objective Assessment of Motor Symptoms in Parkinson’s Disease2015In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 15, no 9, 23727-23744 p.Article in journal (Refereed)
    Abstract [en]

    A challenge for the clinical management of advanced Parkinson’s disease (PD) patients is the emergence of fluctuations in motor performance, which represents a significant source of disability during activities of daily living of the patients. There is a lack of objective measurement of treatment effects for in-clinic and at-home use that can provide an overview of the treatment response. The objective of this paper was to develop a method for objective quantification of advanced PD motor symptoms related to off episodes and peak dose dyskinesia, using spiral data gathered by a touch screen telemetry device. More specifically, the aim was to objectively characterize motor symptoms (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Digitized upper limb movement data of 65 advanced PD patients and 10 healthy (HE) subjects were recorded as they performed spiral drawing tasks on a touch screen device in their home environment settings. Several spatiotemporal features were extracted from the time series and used as inputs to machine learning methods. The methods were validated against ratings on animated spirals scored by four movement disorder specialists who visually assessed a set of kinematic features and the motor symptom. The ability of the method to discriminate between PD patients and HE subjects and the test-retest reliability of the computed scores were also evaluated. Computed scores correlated well with mean visual ratings of individual kinematic features. The best performing classifier (Multilayer Perceptron) classified the motor symptom (bradykinesia or dyskinesia) with an accuracy of 84% and area under the receiver operating characteristics curve of 0.86 in relation to visual classifications of the raters. In addition, the method provided high discriminating power when distinguishing between PD patients and HE subjects as well as had good test-retest reliability. This study demonstrated the potential of using digital spiral analysis for objective quantification of PD-specific and/or treatment-induced motor symptoms.

  • 15.
    Monroy, Javier
    et al.
    Instituto de Investigación Biomedica de Malaga (IBIMA), Universidad de Malaga, Malaga, Spain.
    Hernandez Bennetts, Victor
    Örebro University, School of Science and Technology.
    Fan, Han
    Örebro University, School of Science and Technology.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Gonzales-Jimenez, Javier
    Instituto de Investigación Biomedica de Malaga (IBIMA), Universidad de Malaga, Malaga, Spain.
    GADEN: A 3D Gas Dispersion Simulator for Mobile Robot Olfaction in Realistic Environments2017In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 7, 1479Article in journal (Refereed)
    Abstract [en]

    This work presents a simulation framework developed under the widely used Robot Operating System (ROS) to enable the validation of robotics systems and gas sensing algorithms under realistic environments. The framework is rooted in the principles of computational fluid dynamics and filament dispersion theory, modeling wind flow and gas dispersion in 3D real-world scenarios (i.e., accounting for walls, furniture, etc.). Moreover, it integrates the simulation of different environmental sensors, such as metal oxide gas sensors, photo ionization detectors, or anemometers. We illustrate the potential and applicability of the proposed tool by presenting a simulation case in a complex and realistic office-like environment where gas leaks of different chemicals occur simultaneously. Furthermore, we accomplish quantitative and qualitative validation by comparing our simulated results against real-world data recorded inside a wind tunnel where methane was released under different wind flow profiles. Based on these results, we conclude that our simulation framework can provide a good approximation to real world measurements when advective airflows are present in the environment.

  • 16.
    Mosberger, Rafael
    et al.
    Örebro University, School of Science and Technology.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    A customized vision system for tracking humans wearing reflective safety clothing from industrial vehicles and machinery2014In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, no 10, 17952-17980 p.Article in journal (Refereed)
    Abstract [en]

    This article presents a novel approach for vision-based detection and tracking of humans wearing high-visibility clothing with retro-reflective markers. Addressing industrial applications where heavy vehicles operate in the vicinity of humans, we deploy a customized stereo camera setup with active illumination that allows for efficient detection of the reflective patterns created by the worker's safety garments. After segmenting reflective objects from the image background, the interest regions are described with local image feature descriptors and classified in order to discriminate safety garments from other reflective objects in the scene. In a final step, the trajectories of the detected humans are estimated in 3D space relative to the camera. We evaluate our tracking system in two industrial real-world work environments on several challenging video sequences. The experimental results indicate accurate tracking performance and good robustness towards partial occlusions, body pose variation, and a wide range of different illumination conditions.

  • 17.
    Palumbo, Filippo
    et al.
    Area Ric CNR, ISTI, Natl Res Council, Pisa, Italy; Univ Pisa, Dept Comp Sci, I-56127 Pisa, Italy.
    Ullberg, Jonas
    Örebro University, School of Science and Technology.
    Stimec, Ales
    XLAB Doo, XLAB Res, Ljubljana 1000, Slovenia.
    Furfari, Francesco
    Area Ric CNR, ISTI, Natl Res Council, Pisa, Italy.
    Karlsson, Lars
    Örebro University, School of Science and Technology.
    Coradeschi, Silvia
    Örebro University, School of Science and Technology.
    Sensor Network Infrastructure for a Home Care Monitoring System2014In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 14, no 3, 3833-3860 p.Article in journal (Refereed)
    Abstract [en]

    This paper presents the sensor network infrastructure for a home care system that allows long-term monitoring of physiological data and everyday activities. The aim of the proposed system is to allow the elderly to live longer in their home without compromising safety and ensuring the detection of health problems. The system offers the possibility of a virtual visit via a teleoperated robot. During the visit, physiological data and activities occurring during a period of time can be discussed. These data are collected from physiological sensors (e.g., temperature, blood pressure, glucose) and environmental sensors (e.g., motion, bed/chair occupancy, electrical usage). The system can also give alarms if sudden problems occur, like a fall, and warnings based on more long-term trends, such as the deterioration of health being detected. It has been implemented and tested in a test environment and has been deployed in six real homes for a year-long evaluation. The key contribution of the paper is the presentation of an implemented system for ambient assisted living (AAL) tested in a real environment, combining the acquisition of sensor data, a flexible and adaptable middleware compliant with the OSGistandard and a context recognition application. The system has been developed in a European project called GiraffPlus.

  • 18.
    Pashami, Sepideh
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    TREFEX: trend estimation and change detection in the response of mox gas sensors2013In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 13, no 6, 7323-7344 p.Article in journal (Refereed)
    Abstract [en]

    Many applications of metal oxide gas sensors can benefit from reliable algorithmsto detect significant changes in the sensor response. Significant changes indicate a changein the emission modality of a distant gas source and occur due to a sudden change ofconcentration or exposure to a different compound. As a consequence of turbulent gastransport and the relatively slow response and recovery times of metal oxide sensors,their response in open sampling configuration exhibits strong fluctuations that interferewith the changes of interest. In this paper we introduce TREFEX, a novel change pointdetection algorithm, especially designed for metal oxide gas sensors in an open samplingsystem. TREFEX models the response of MOX sensors as a piecewise exponentialsignal and considers the junctions between consecutive exponentials as change points. Weformulate non-linear trend filtering and change point detection as a parameter-free convexoptimization problem for single sensors and sensor arrays. We evaluate the performanceof the TREFEX algorithm experimentally for different metal oxide sensors and severalgas emission profiles. A comparison with the previously proposed GLR method shows aclearly superior performance of the TREFEX algorithm both in detection performance andin estimating the change time.

  • 19.
    Pashami, Sepideh
    et al.
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    Trincavelli, Marco
    Örebro University, School of Science and Technology.
    Detecting changes of a distant gas source with an array of MOX gas sensors2012In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 12, no 12, 16404-16419 p.Article in journal (Refereed)
    Abstract [en]

    We address the problem of detecting changes in the activity of a distant gas source from the response of an array of metal oxide (MOX) gas sensors deployed in an open sampling system. The main challenge is the turbulent nature of gas dispersion and the response dynamics of the sensors. We propose a change point detection approach and evaluate it on individual gas sensors in an experimental setup where a gas source changes in intensity, compound, or mixture ratio. We also introduce an efficient sensor selection algorithm and evaluate the change point detection approach with the selected sensor array subsets.

  • 20.
    Rituerto, Alejandro
    et al.
    Instituto de Investigación en Ingeniería de Aragón, Deptartmento de Informática e Ingeniería de Sistemas, University of Zaragoza, Zaragoza, Spain.
    Andreasson, Henrik
    Örebro University, School of Science and Technology.
    Murillo, Ana C.
    Instituto de Investigación en Ingeniería de Aragón, Deptartmento de Informática e Ingeniería de Sistemas, University of Zaragoza, Zaragoza, Spain.
    Lilienthal, Achim
    Örebro University, School of Science and Technology.
    Jesus Guerrero, Jose
    Instituto de Investigación en Ingeniería de Aragón, Deptartmento de Informática e Ingeniería de Sistemas, University of Zaragoza, Zaragoza, Spain.
    Building an Enhanced Vocabulary of the Robot Environment with a Ceiling Pointing Camera2016In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 16, no 4, 493Article in journal (Refereed)
    Abstract [en]

    Mobile robots are of great help for automatic monitoring tasks in different environments. One of the first tasks that needs to be addressed when creating these kinds of robotic systems is modeling the robot environment. This work proposes a pipeline to build an enhanced visual model of a robot environment indoors. Vision based recognition approaches frequently use quantized feature spaces, commonly known as Bag of Words (BoW) or vocabulary representations. A drawback using standard BoW approaches is that semantic information is not considered as a criteria to create the visual words. To solve this challenging task, this paper studies how to leverage the standard vocabulary construction process to obtain a more meaningful visual vocabulary of the robot work environment using image sequences. We take advantage of spatio-temporal constraints and prior knowledge about the position of the camera. The key contribution of our work is the definition of a new pipeline to create a model of the environment. This pipeline incorporates (1) tracking information to the process of vocabulary construction and (2) geometric cues to the appearance descriptors. Motivated by long term robotic applications, such as the aforementioned monitoring tasks, we focus on a configuration where the robot camera points to the ceiling, which captures more stable regions of the environment. The experimental validation shows how our vocabulary models the environment in more detail than standard vocabulary approaches, without loss of recognition performance. We show different robotic tasks that could benefit of the use of our visual vocabulary approach, such as place recognition or object discovery. For this validation, we use our publicly available data-set.

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