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  • 1. Ackum, Susanne
    et al.
    Borg, Tor
    Braunerhjelm, Pontus
    Calmfors, Lars
    Institutet för näringslivsforskning, Stockholm, Sweden.
    Eklund, Klas
    Hansson, Åsa
    Lunds Universitet, Institutet för näringslivsforskning, Lund, Sweden.
    Hultkrantz, Lars
    Örebro University, Örebro University School of Business.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Nordström Skans, Oskar
    Wetterstrand, Maria
    Vi tar fram en handfast plan för en omstart av Sverige2020In: Dagens Nyheter, ISSN 1101-2447, no 27 aprilArticle in journal (Other (popular science, discussion, etc.))
    Abstract [sv]

    Det är /.../ fullt möjligt att tänka strategiskt och systematiskt även i brinnande kris. Omstartskommissionen hoppas kunna bidra till fokus, analys och konkreta policyförslag för att stödja Sveriges långsiktiga inriktning. Vi kommer att under våren och sommaren anordna seminarier och hearings om vårt arbete, delrapporter ska läggas fram – och när budgetarbetet börjar och Riksdagen öppnar, vill vi kunna bidra med en rejäl och handfast plan för hur vi omstartar Sverige.

  • 2.
    Ahmed, Mobyen Uddin
    et al.
    Örebro University, School of Science and Technology.
    Banaee, Hadi
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Health monitoring for elderly: an application using case-based reasoning and cluster analysis2013In: ISRN Artificial Intelligence, ISSN 2090-7435, E-ISSN 2090-7443, Vol. 2013, no 2013, p. 1-11Article in journal (Refereed)
    Abstract [en]

    This paper presents a framework to process and analyze data from a pulse oximeter which measures pulse rate and blood oxygen saturation from a set of individuals remotely. Using case-based reasoning (CBR) as the backbone to the framework, records are analyzed and categorized according to how well they are similar. Record collection has been performed using a personalized health profiling approach where participants wore a pulse oximeter sensor for a fixed period of time and performed specific activities for pre-determined intervals. Using a variety of feature extraction in time, frequency and time-frequency domains, and data processing techniques, the data is fed into a CBR system which retrieves most similar cases and generates alarm and flag according to the case outcomes. The system has been compared with an expert's classification and 90% match is achieved between the expert's and CBR classification. Again, considering the clustered measurements the CBR approach classifies 93% correctly both for the pulse rate and oxygen saturation. Along with the proposed methodology, this paper provides a basis for which the system can be used in analysis of continuous health monitoring and be used as a suitable method as in home/remote monitoring systems.

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  • 3.
    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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  • 4.
    Ahmed, Mobyen Uddin
    et al.
    Örebro University, School of Science and Technology.
    Islam, Asif Moinul
    Örebro University, Örebro, Sweden.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    A case-based patient identification system using pulseoximeter and a personalized health profile2012Conference paper (Refereed)
    Abstract [en]

    This paper proposes a case-based system framework in order to identify patient using their health parameters taken with physiological sensors. It combines a personalized health profiling protocol with a Case-Based Reasoning (CBR) approach. The personalized health profiling helps to determine a number of individual parameters which are important inputs for a clinician to make the final diagnosis and treatment plan. The proposed system uses a pulse oximeter that measures pulse rate and blood oxygen saturation. The measurements are taken through an android application in a smart phone which is connected with the pulseoximeter and bluetooth communication. The CBR approach helps clinicians to make a diagnosis, classification and treatment plan by retrieving the most similar previous case. The case may also be used to follow the treatment progress. Here, the cases are formulated with person’s contextual information and extracted features from sensor signal measurements. The features are extracted considering three domain analysis:1) time domain features using statistical measurement, 2) frequency domain features applying Fast Fourier Transform (FFT), and 3) time-frequency domain features applying Discrete Wavelet Transform (DWT). The initial result is acceptable that shows the advancement of the system while combining the personalized health profiling together with CBR.

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    fulltext
  • 5.
    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%.

  • 6.
    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

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    Physical activity identification using supervised machine learning and based on pulse rate
  • 7.
    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.

  • 8.
    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.

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    An Evaluation Tool of the Effect of Robots in Eldercare on the Sense of Safety and Security
  • 9.
    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.
    Enhancing Social Human-Robot Interaction with Deep Reinforcement Learning.2018In: Proc. FAIM/ISCA Workshop on Artificial Intelligence for Multimodal Human Robot Interaction, 2018, MHRI , 2018, p. 48-50Conference paper (Refereed)
    Abstract [en]

    This research aims to develop an autonomous social robot for elderly individuals. The robot will learn from the interaction and change its behaviors in order to enhance the interaction and improve the user experience. For this purpose, we aim to use Deep Reinforcement Learning. The robot will observe the user’s verbal and nonverbal social cues by using its camera and microphone, the reward will be positive valence and engagement of the user.

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    Enhancing Social Human-Robot Interaction with Deep Reinforcement Learning
  • 10.
    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.

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    The Relevance of Social Cues in Assistive Training with a Social Robot
  • 11.
    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.

  • 12.
    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)
  • 13.
    Akalin, Neziha
    et al.
    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.
    Do you feel safe with your robot? Factors influencing perceived safety in human-robot interaction based on subjective and objective measures2022In: International journal of human-computer studies, ISSN 1071-5819, E-ISSN 1095-9300, Vol. 158, article id 102744Article in journal (Refereed)
    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.

  • 14.
    Akalin, Neziha
    et al.
    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.
    Evaluating the Sense of Safety and Security in Human - Robot Interaction with Older People2019In: 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.

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    Evaluating the Sense of Safety and Security in Human - Robot Interaction with Older People
  • 15.
    Akalin, Neziha
    et al.
    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.
    The Influence of Feedback Type in Robot-Assisted Training2019In: Multimodal Technologies and Interaction, E-ISSN 2414-4088, Vol. 3, no 4Article in journal (Refereed)
    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.

  • 16.
    Akalin, Neziha
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Reinforcement Learning Approaches in Social Robotics2021In: Sensors, E-ISSN 1424-8220, Vol. 21, no 4, article id 1292Article, review/survey (Refereed)
    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.

  • 17.
    Alexopoulou, Sofia
    et al.
    Örebro University, School of Humanities, Education and Social Sciences.
    Fart, Frida
    Örebro University, School of Medical Sciences.
    Jonsson, Ann-Sofie
    Örebro University, School of Hospitality, Culinary Arts & Meal Science.
    Karni, Liran
    Örebro University, Örebro University School of Business.
    Kenalemang, Lame Maatla
    Örebro University, School of Humanities, Education and Social Sciences.
    Krishna, Sai
    Örebro University, School of Science and Technology.
    Lindblad, Katarina
    Örebro University, School of Music, Theatre and Art.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Lundin, Elin
    Örebro University, School of Health Sciences.
    Samzelius, Hanna
    Örebro University, School of Humanities, Education and Social Sciences.
    Schoultz, Magnus
    Örebro University, School of Humanities, Education and Social Sciences.
    Spang, Lisa
    Örebro University, School of Health Sciences.
    Söderman, Annika
    Örebro University, School of Health Sciences.
    Tarum, Janelle
    Örebro University, School of Health Sciences.
    Tsertsidis, Antonios
    Örebro University, Örebro University School of Business.
    Widell, Bettina
    Örebro University, School of Humanities, Education and Social Sciences.
    Nilsson, Kerstin (Editor)
    Örebro University, School of Medical Sciences.
    Successful ageing in an interdisciplinary context: popular science presentations2018Book (Other (popular science, discussion, etc.))
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    Omslag
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    Successful ageing in an interdisciplinary context: popular science presentations
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  • 18.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Klügl, Franziska
    Örebro University, School of Science and Technology. Örebro University, School of Law, Psychology and Social Work.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Exploiting Context and Semantics for UAV Path-finding in an Urban Setting2017In: Proceedings of the 1st International Workshop on Application of Semantic Web technologies in Robotics (AnSWeR 2017), Portoroz, Slovenia, May 29th, 2017 / [ed] Emanuele Bastianelli, Mathieu d'Aquin, Daniele Nardi, Technical University Aachen , 2017, p. 11-20Conference paper (Refereed)
    Abstract [en]

    In this paper we propose an ontology pattern that represents paths in a geo-representation model to be used in an aerial path planning processes. This pattern provides semantics related to constraints (i.e., ight forbidden zones) in a path planning problem in order to generate collision free paths. Our proposed approach has been applied on an ontology containing geo-regions extracted from satellite imagery data from a large urban city as an illustrative example.

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    Exploiting Context and Semantics for UAV Path-finding in an Urban Setting
  • 19.
    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, E-ISSN 1424-8220, Vol. 17, no 11, article id 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.

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    fulltext
  • 20.
    Alirezaie, Marjan
    et al.
    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.
    Knowing without telling: integrating sensing and mapping for creating an artificial companion2016In: Proceedings of the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, New York, NY, USA: Association for Computing Machinery (ACM), 2016, p. 11:1-11:4Conference paper (Refereed)
    Abstract [en]

    This paper depicts a sensor-based map navigation approach which targets users, who due to disabilities or lack of technical knowledge are currently not in the focus of map system developments for personalized information. What differentiates our approach from the state-of-art mostly integrating localized social media data, is that our vision is to integrate real time sensor generated data that indicates the situation of dfferent phenomena (such as the physiological functions of the body) related to the user. The challenge hereby is mainly related to knowledge representation and integration. The tentative impact of our vision for future navigation systems is re ected within a scenario.

  • 21.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Automated reasoning using abduction for interpretation of medical signals2014In: Journal of Biomedical Semantics, E-ISSN 2041-1480, Vol. 5, article id 35Article in journal (Refereed)
    Abstract [en]

    This paper proposes an approach to leverage upon existing ontologies in order to automate the annotation of time series medical data. The annotation is achieved by an abductive reasoner using parsimonious covering theorem in order to determine the best explanation or annotation for specific user defined events in the data. The novelty of this approach resides in part by the system’s flexibility in how events are defined by users and later detected by the system. This is achieved via the use of different ontologies which find relations between medical, lexical and numerical concepts. A second contribution resides in the application of an abductive reasoner which uses the online and existing ontologies to provide annotations. The proposed method is evaluated on datasets collected from ICU patients and the generated annotations are compared against those given by medical experts.

  • 22.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Automatic annotation of sensor data streams using abductive reasoning2013In: Proceedings of the International Conference on Knowledge Engineering and Ontology Development, SciTePress, 2013, p. 345-354Conference paper (Refereed)
  • 23.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Ontology alignment for classification of low level sensor data2012Conference paper (Refereed)
  • 24.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Reasoning for Improved Sensor Data Interpretation in a Smart Home2014Conference paper (Refereed)
    Abstract [en]

    In this paper an ontological representation and reasoning paradigm has been proposed for interpretation of time-series signals. The signals come from sensors observing a smart environment. The signal chosen for the annotation process is a set of unintuitive and complexgas sensor data. The ontology of this paradigm is inspired form the SSNontology (Semantic Sensor Network) and used for representation of both the sensor data and the contextual information. The interpretation process is mainly done by an incremental ASP solver which as input receivesa logic program that is generated from the contents of the ontology. The contextual information together with high level domain knowledge given in the ontology are used to infer explanations (answer sets) for changes in the ambient air detected by the gas sensors.

  • 25.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Reasoning for sensor data interpretation: an application to air quality monitoring2015In: Journal of Ambient Intelligence and Smart Environments, ISSN 1876-1364, E-ISSN 1876-1372, Vol. 7, no 4, p. 579-597Article in journal (Refereed)
    Abstract [en]

    In this paper we introduce a representation and reasoning model for the interpretation of time-series signals of a gas sensor situated in a sensor network. The interpretation process includes inferring high level explanations for changes detected over the gas signals. Inspired from the Semantic Sensor Network (SSN), the ontology used in this work provides an adaptive way of modelling the domain-related knowledge. Furthermore, exploiting (Incremental) Answer Set Programming (ASP) enables a declarative and automatic way of rule definition. Converting the ontology concepts and relations into ASP logic programs, the interpretation process defines a logic program whose answer sets are considered as eventual explanations for the detected changes in the gas sensor signals. The proposed approach is tested in a kitchen environment which contains several objects monitored by different sensors. The contextual information provided by the sensor network together with high level domain knowledge are used to infer explanations for changes in the ambient air detected by the gas sensors.

  • 26.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Towards Automatic Ontology Alignment for Enriching Sensor Data Analysis2013In: Communications in Computer and Information Science, ISSN 1865-0929, E-ISSN 1865-0937, Vol. 415, p. 179-193Article in journal (Refereed)
    Abstract [en]

    In this work ontology alignment is used to align an ontology comprising high level knowledge to a structure representing the results of low-level sensor data classification. To resolve inherent uncertainties from the data driven classifier, an ontology about application domain is aligned to the classifier output and the result is recommendation system able to suggest a course of action that will resolve the uncertainty. This work is instantiated in a medical application domain where signals from an electronic nose are classified into different bacteria types. In case of misclassifications resulting from the data driven classifier, the alignment to an ontology representing traditional microbiology tests suggests a subset of tests most relevant to use. The result is a hybrid classification system (electronic nose and traditional testing) that automatically exploits domain knowledge in the identification process.

  • 27.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Kiselev, Andrey
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Open GeoSpatial Data as a Source of Ground Truth for Automated Labelling of Satellite Images2016In: SDW 2016: Spatial Data on the Web, Proceedings / [ed] Krzysztof Janowicz et al., CEUR Workshop Proceedings , 2016, p. 5-8Conference paper (Refereed)
  • 28.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Knowledge Representation and Reasoning Methods to Explain Errors in Machine Learning2020In: Knowledge Graphs for eXplainable Artificial Intelligence: Foundations, Applications and Challenges / [ed] Ilaria Tiddi, Freddy Lécué, Pascal Hitzler, IOS Press, 2020Chapter in book (Refereed)
    Abstract [en]

    In this chapter we focus the use of knowledge representation and reasoning (KRR) methods as a guide to machine learning algorithms whereby relevant contextual knowledge can be leveraged upon. In this way, the learning methods improve performance by taking into account causal relationships behind errors. Performance improvement can be obtained by focusing the learning task on aspects that are particularly challenging (or prone to error), and then using added knowledge inferred by the reasoner as a means to provide further input to learning algorithms. Said differently, the KRR algorithms guide the learning algorithms, feeding it labels and data in order to iteratively reduce the errors calculated by a given cost function. This closed loop system comes with the added benefit that errors are also made more understandable to the human, as it is the task of the KRR system to contextualize the errors from the ML algorithm in accordance with its knowledge model. This represents a type of explainable AI that is focused on interpretability. This chapter will discuss the benefits of using KRR methods with ML methods in this way, and demonstrate an approach applied to satellite data for the purpose of improving classification and segmentation task.

  • 29.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Sioutis, Michael
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    A Symbolic Approach for Explaining Errors in Image Classification Tasks2018Conference paper (Refereed)
    Abstract [en]

    Machine learning algorithms, despite their increasing success in handling object recognition tasks, still seldom perform without error. Often the process of understanding why the algorithm has failed is the task of the human who, using domain knowledge and contextual information, can discover systematic shortcomings in either the data or the algorithm. This paper presents an approach where the process of reasoning about errors emerging from a machine learning framework is automated using symbolic techniques. By utilizing spatial and geometrical reasoning between objects in a scene, the system is able to describe misclassified regions in relation to its context. The system is demonstrated in the remote sensing domain where objects and entities are detected in satellite images.

  • 30.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Längkvist, Martin
    Örebro University, School of Science and Technology.
    Sioutis, Michael
    Department of Computer Science, Aalto University, Espoo, Finland.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Semantic Referee: A Neural-Symbolic Framework for Enhancing Geospatial Semantic Segmentation2019In: Semantic Web, ISSN 1570-0844, E-ISSN 2210-4968, Vol. 10, no 5, p. 863-880Article in journal (Refereed)
    Abstract [en]

    Understanding why machine learning algorithms may fail is usually the task of the human expert that uses domain knowledge and contextual information to discover systematic shortcomings in either the data or the algorithm. In this paper, we propose a semantic referee, which is able to extract qualitative features of the errors emerging from deep machine learning frameworks and suggest corrections. The semantic referee relies on ontological reasoning about spatial knowledge in order to characterize errors in terms of their spatial relations with the environment. Using semantics, the reasoner interacts with the learning algorithm as a supervisor. In this paper, the proposed method of the interaction between a neural network classifier and a semantic referee shows how to improve the performance of semantic segmentation for satellite imagery data.

  • 31.
    Alirezaie, Marjan
    et al.
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Context Recognition: Towards Automatic Query Generation2015In: Ambient Intelligence: 12th European Conference, AmI 2015, Athens, Greece, November 11-13, 2015, Proceedings, Springer, 2015, p. 205-218Conference paper (Refereed)
    Abstract [en]

    In this paper, we present an ontology-based approach in designing knowledge model for context recognition (CR) systems. The main focus in this paper is on the use of an ontology to facilitate the generation of user-based queries to the CR system. By leveraging from the ontology, users need not know about sensor details and the structure of the ontology in expressing queries related to events of interest. To validate the approach and demonstrate the flexibility of the ontology for query generation, the ontology has been integrated in two separate application domains. The first domain considers a health care system implemented for the GiraffPlus project where the query generation process is automated to request information about activities of daily living. The second application uses the same ontology for an air quality monitoring application in the home. Since these two systems are independently developed for different purposes, the ease of applying the ontology upon them can be considered as a credit for its generality.

  • 32.
    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, E-ISSN 1424-8220, Vol. 17, no 7, article id 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.

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    fulltext
  • 33.
    Asadi, Sahar
    et al.
    Örebro University, School of Science and Technology.
    Pashami, Sepideh
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Lilienthal, Achim J.
    Örebro University, School of Science and Technology.
    TD Kernel DM+V: time-dependent statistical gas distribution modelling on simulated measurements2011In: Olfaction and Electronic Nose: proceedings of the 14th International Symposium on Olfaction and Electronic Nose (ISOEN) / [ed] Perena Gouma, Springer Science+Business Media B.V., 2011, p. 281-282Conference paper (Refereed)
    Abstract [en]

    To study gas dispersion, several statistical gas distribution modelling approaches have been proposed recently. A crucial assumption in these approaches is that gas distribution models are learned from measurements that are generated by a time-invariant random process. While a time-independent random process can capture certain fluctuations in the gas distribution, more accurate models can be obtained by modelling changes in the random process over time. In this work we propose a time-scale parameter that relates the age of measurements to their validity for building the gas distribution model in a recency function. The parameters of the recency function define a time-scale and can be learned. The time-scale represents a compromise between two conflicting requirements for obtaining accurate gas distribution models: using as many measurements as possible and using only very recent measurements. We have studied several recency functions in a time-dependent extension of the Kernel DM+V algorithm (TD Kernel DM+V). Based on real-world experiments and simulations of gas dispersal (presented in this paper) we demonstrate that TD Kernel DM+V improves the obtained gas distribution models in dynamic situations. This represents an important step towards statistical modelling of evolving gas distributions.

    Download full text (pdf)
    fulltext
  • 34.
    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.
    A framework for automatic text generation of trends in physiological time series data2013In: IEEE International Conference on Systems, Man, and Cybernetics, 13-16 Oct. 2013, Manchester, IEEE conference proceedings, 2013, p. 3876-3881Conference paper (Refereed)
    Abstract [en]

    Health monitoring systems using wearable sensorshave rapidly grown in the biomedical community. The mainchallenges in physiological data monitoring are to analyse largevolumes of health measurements and to represent the acquiredinformation. Natural language generation is an effective methodto create summaries for both clinicians and patients as it candescribe useful information extracted from sensor data in textualformat. This paper presents a framework of a natural languagegeneration system that provides a text-based representation ofthe extracted numeric information from physiological sensorsignals. More specifically, a new partial trend detection algorithmis introduced to capture the particular changes and events ofhealth parameters. The extracted information is then representedconsidering linguistic characterisation of numeric features. Ex-perimental analysis was performed using a wearable sensor and demonstrates a possible output in natural language text.

  • 35.
    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, E-ISSN 1424-8220, Vol. 13, no 12, p. 17472-17500Article, review/survey (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

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    banaee_etal_sensors2013
  • 36.
    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.
    Descriptive Modelling of Clinical Conditions with Data-driven Rule Mining in Physiological Data2015In: Proceedings of the 8th International conference of Health Informatics (HEALTHINF 2015), SciTePress, 2015Conference paper (Refereed)
    Abstract [en]

    This paper presents an approach to automatically mine rules in time series data representing physiologicalparameters in clinical conditions. The approach is fully data driven, where prototypical patterns are mined foreach physiological time series data. The generated rules based on the prototypical patterns are then describedin a textual representation which captures trends in each physiological parameter and their relation to the otherphysiological data. In this paper, a method for measuring similarity of rule sets is introduced in order tovalidate the uniqueness of rule sets. This method is evaluated on physiological records from clinical classesin the MIMIC online database such as angina, sepsis, respiratory failure, etc.. The results show that the rulemining technique is able to acquire a distinctive model for each clinical condition, and represent the generatedrules in a human understandable textual representation

    Download full text (pdf)
    fulltext
  • 37.
    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.
    Towards NLG for Physiological Data Monitoring with Body Area Networks2013In: 14th European Workshop on Natural Language Generation, 2013, p. 193-197Conference paper (Refereed)
    Abstract [en]

    This position paper presents an on-goingwork on a natural language generationframework that is particularly tailored fornatural language generation from bodyarea networks. We present an overview ofthe main challenges when considering thistype of sensor devices used for at homemonitoring of health parameters. The paperpresents the first steps towards the implementationof a system which collectsinformation from heart rate and respirationusing a wearable sensor.

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    fulltext
  • 38.
    Banaee, Hadi
    et al.
    Örebro University, School of Science and Technology.
    Chimamiwa, Gibson
    Örebro University, School of Science and Technology.
    Alirezaie, Marjan
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Explaining Habits and Changes of Activities in Smart Homes2020Conference paper (Refereed)
    Abstract [en]

    Early cognitive deterioration can emerge in the form of changes in daily habits and there is a need to go beyond activity recognition for recognising habits and detecting changes in smart homes. In this paper, we propose a system composed of 1) data-driven habit recognition, 2) change detection in the trends of habits, and 3) linguistic descriptions of both habits and changes. Our habit recognition approach relies on both attribute-based analysis and association-based analysis. The generated outputs of the habit recognition and change detection are finally interpreted in linguistic descriptions for the end-users of the system.

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    Explaining Habits and Changes of Activities in Smart Homes
  • 39.
    Banaee, Hadi
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Data-driven rule mining and representation of temporal patterns in physiological sensor data2015In: IEEE journal of biomedical and health informatics, ISSN 2168-2194, E-ISSN 2168-2208, Vol. 19, no 5, p. 1557-1566Article in journal (Refereed)
    Abstract [en]

    Mining and representation of qualitative patterns is a growing field in sensor data analytics. This paper leverages from rule mining techniques to extract and represent temporal relation of prototypical patterns in clinical data streams. The approach is fully data-driven, where the temporal rules are mined from physiological time series such as heart rate, respiration rate, and blood pressure. To validate the rules, a novel similarity method is introduced, that compares the similarity between rule sets. An additional aspect of the proposed approach has been to utilize natural language generation techniques to represent the temporal relations between patterns. In this study, the sensor data in the MIMIC online database was used for evaluation, in which the mined temporal rules as they relate to various clinical conditions (respiratory failure, angina, sepsis, ...) were made explicit as a textual representation. Furthermore, it was shown that the extracted rule set for any particular clinical condition was distinct from other clinical conditions.

  • 40.
    Banaee, Hadi
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Using Conceptual Spaces to Model Domain Knowledge in Data-to-Text Systems2014In: Proceedings of the 8th International Natural Language Generation Conference, Association for Computational Linguistics, 2014, p. 11-15Conference paper (Refereed)
    Abstract [en]

    This position paper introduces the utilityof the conceptual spaces theory to conceptualisethe acquired knowledge in data-totextsystems. A use case of the proposedmethod is presented for text generationsystems dealing with sensor data. Modellinginformation in a conceptual spaceexploits a spatial representation of domainknowledge in order to perceive unexpectedobservations. This ongoing work aimsto apply conceptual spaces in NLG forgrounding numeric information into thesymbolic representation and confrontingthe important step of acquiring adequateknowledge in data-to-text systems.

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    Banaee_INLG2014
  • 41.
    Banaee, Hadi
    et al.
    Örebro University, School of Science and Technology.
    Schaffernicht, Erik
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Data-Driven Conceptual Spaces: Creating Semantic Representations for Linguistic Descriptions of Numerical Data2018In: The journal of artificial intelligence research, ISSN 1076-9757, E-ISSN 1943-5037, Vol. 63, p. 691-742Article in journal (Refereed)
    Abstract [en]

    There is an increasing need to derive semantics from real-world observations to facilitate natural information sharing between machine and human. Conceptual spaces theory is a possible approach and has been proposed as mid-level representation between symbolic and sub-symbolic representations, whereby concepts are represented in a geometrical space that is characterised by a number of quality dimensions. Currently, much of the work has demonstrated how conceptual spaces are created in a knowledge-driven manner, relying on prior knowledge to form concepts and identify quality dimensions. This paper presents a method to create semantic representations using data-driven conceptual spaces which are then used to derive linguistic descriptions of numerical data. Our contribution is a principled approach to automatically construct a conceptual space from a set of known observations wherein the quality dimensions and domains are not known a priori. This novelty of the approach is the ability to select and group semantic features to discriminate between concepts in a data-driven manner while preserving the semantic interpretation that is needed to infer linguistic descriptions for interaction with humans. Two data sets representing leaf images and time series signals are used to evaluate the method. An empirical evaluation for each case study assesses how well linguistic descriptions generated from the conceptual spaces identify unknown observations. Furthermore,  comparisons are made with descriptions derived on alternative approaches for generating semantic models.

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    Data Driven Conceptual Spaces, Banaee et. al, JAIR 2018
  • 42.
    Beeson, Patrick
    et al.
    TRACLabs Inc., Webster TX, USA.
    Kortenkamp, David
    TRACLabs Inc., Webster TX, USA.
    Bonasso, R. Peter
    TRACLabs Inc., Webster TX, USA.
    Persson, Andreas
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Bona, Jonathan P
    State University of New York, Buffalo, USA.
    An Ontology-Based Symbol Grounding System for Human-Robot Interaction2014In: Artificial Intelligence for Human-Robot Interaction: 2014 AAAI Fall Symposium, AAAI Press, 2014, p. 48-50Conference paper (Refereed)
    Abstract [en]

    This paper presents an ongoing collaboration to develop a perceptual anchoring framework which creates and maintains the symbol-percept links concerning household objects. The paper presents an approach to non-trivialize the symbol system using ontologies and allow for HRI via enabling queries about objects properties, their affordances, and their perceptual characteristics as viewed from the robot (e.g. last seen). This position paper describes in brief the objective of creating a long term perceptual anchoring framework for HRI and outlines the preliminary work done this far.

  • 43.
    Blad, Samuel
    et al.
    Örebro University, School of Science and Technology. Nexer.
    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.
    Empirical analysis of the convergence of Double DQN in relation to reward sparsity2022In: 21st IEEE International Conference on Machine Learning and Applications. ICMLA 2022: Proceedings / [ed] Wani, MA; Kantardzic, M; Palade, V; Neagu, D; Yang, L; Chan, KY, IEEE, 2022, p. 591-596Conference paper (Refereed)
    Abstract [en]

    Q-Networks are used in Reinforcement Learning to model the expected return from every action at a given state. When training Q-Networks, external reward signals are propagated to the previously performed actions leading up to each reward. If many actions are required before experiencing a reward, the reward signal is distributed across all those actions, where some actions may have greater impact on the reward than others. As the number of significant actions between rewards increases, the relative importance of each action decreases. If actions have too small importance, their impact might be over-shadowed by noise in a deep neural network model, potentially causing convergence issues. In this work, we empirically test the limits of increasing the number of actions leading up to a reward in a simple grid-world environment. We show in our experiments that even though the training error surpasses the reward signal attributed to each action, the model is still able to learn a smooth enough value representation.

  • 44.
    Broxvall, Mathias
    et al.
    Örebro University, Department of Technology.
    Coradeschi, Silvia
    Örebro University, Department of Technology.
    Loutfi, Amy
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    An ecological approach to odour recognition in intelligent environments2006In: 2006 IEEE International Conference on Robotics and automation, ICRA 2006, 2006, p. 2066-2071Conference paper (Refereed)
    Abstract [en]

    We present a new approach for odour detection and recognition based on a so-called PEIS-Ecology: a network of gas sensors and a mobile robot are integrated in an intelligent environment. The environment can provide information regarding the location of potential odour sources, which is then relayed to a mobile robot equipped with an electronic nose. The robot can then perform a more thorough analysis of the odour character. This is a novel approach which alleviates some the challenges in mobile olfaction techniques by single and embedded mobile robots. The environment also provides contextual information which can be used to constrain the learning of odours, which is shown to improve classification performance.

  • 45.
    Broxvall, Mathias
    et al.
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Interacting with a robot ecology using task templates2007In: 2007 RO-MAN: 16TH IEEE INTERNATIONAL SYMPOSIUM ON ROBOT AND HUMAN INTERACTIVE COMMUNICATION, VOLS 1-3, NEW YORK: IEEE , 2007, p. 486-491Chapter in book (Other academic)
    Abstract [en]

    Robot ecologies provide a new paradigm for assistive, service, industrial, and entertainment robotics which is quickly gaining popularity. These ecologies contain a large number of robotic components pervasively embedded in the environment and interacting with each other. Human users of such systems need to be able to interface with both the system as a whole and, if desired, which each individual component. The humans should be able to transmit, in a natural way, commands that range from basic ones, such as ''turn on the lights in the bedroom'', to abstract ones, such as ''bring me a cup of coffee''. Human users may also need to interact with task execution especially at decision points. In this paper, we introduce an approach to interface a human user to a specific type of robot ecology, called an ecology of Physically Embedded Intelligent Systems, or PEIS-Ecology. The ecology includes simple sensors and actuators and more complicated devices such as mobile robots. The proposed interface satisfies two requirements: 1) to easily and automatically generate component interfaces, and 2) to provide a simple mechanism by which to request and monitor the execution of tasks in the ecology.

  • 46.
    Broxvall, Mathias
    et al.
    Örebro University, Department of Technology.
    Loutfi, Amy
    Örebro University, Department of Technology.
    Saffiotti, Alessandro
    Örebro University, Department of Technology.
    Interacting with a robot ecology using task templates2007In: 16th IEEE international symposium on robot and human interactive communication, RO-MAN 2007, New York: IEEE , 2007, p. 487-492Conference paper (Refereed)
    Abstract [en]

    Robot ecologies provide a new paradigm for assistive, service, industrial, and entertainment robotics which is quickly gaining popularity. These ecologies contain a large number of robotic components pervasively embedded in the environment and interacting with each other. Human users of such systems need to be able to interface with both the system as a w hole and, if desired, which each individual component. The humans should be able to transmit, in a natural way, commands that range from basic ones, such as "turn on the lights in the bedroom", to abstract ones, such as "bring me a cup of coffee". Human users may also need to interact with task execution, especially at decision points. In this paper, we introduce an approach to interface a human user to a specific type of robot ecology, called an ecology of Physically Embedded Intelligent Systems, or PEIS-Ecology. The ecology includes simple sensors and actuators and more complicated devices such as mobile robots. The proposed interface satisfies two requirements: 1) to easily and automatically generate component interfaces, and 2) to provide a simple mechanism by which to request and monitor the execution of tasks in the ecology.

  • 47.
    Can, Ozan Arkan
    et al.
    Koc University.
    Zuidberg Dos Martires, Pedro
    KU Leuven.
    Persson, Andreas
    Örebro University, School of Science and Technology.
    Gaal, Julian
    Osnabrück University.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    De Raedt, Luc
    KU Leuven.
    Yuret, Deniz
    Koc University.
    Saffiotti, Alessandro
    Örebro University, School of Science and Technology.
    Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations2019In: Proceedings of the Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP) / [ed] Archna Bhatia, Yonatan Bisk, Parisa Kordjamshidi, Jesse Thomason, Association for Computational Linguistics , 2019, p. 29-39, article id W19-1604Conference paper (Refereed)
    Abstract [en]

    Human-robot interaction often occurs in the form of instructions given from a human to a robot. For a robot to successfully follow instructions, a common representation of the world and objects in it should be shared between humans and the robot so that the instructions can be grounded. Achieving this representation can be done via learning, where both the world representation and the language grounding are learned simultaneously. However, in robotics this can be a difficult task due to the cost and scarcity of data. In this paper, we tackle the problem by separately learning the world representation of the robot and the language grounding. While this approach can address the challenges in getting sufficient data, it may give rise to inconsistencies between both learned components. Therefore, we further propose Bayesian learning to resolve such inconsistencies between the natural language grounding and a robot’s world representation by exploiting spatio-relational information that is implicitly present in instructions given by a human. Moreover, we demonstrate the feasibility of our approach on a scenario involving a robotic arm in the physical world.

  • 48.
    Chimamiwa, Gibson
    et al.
    Örebro University, School of Science and Technology.
    Alirezaie, Marjan
    Örebro University, School of Science and Technology.
    Banaee, Hadi
    Örebro University, School of Science and Technology.
    Köckemann, Uwe
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Towards Habit Recognition in Smart Homes for People with Dementia2019In: Ambient Intelligence: 15th European Conference, AmI 2019, Rome, Italy, November 13–15, 2019, Proceedings / [ed] Ioannis Chatzigiannakis, Boris De Ruyter, Irene Mavrommati, Springer Nature, 2019, Vol. 11912, p. 363-369Conference paper (Refereed)
    Abstract [en]

    The demand for smart home technologies that enable ageingin place is rising. Through activity recognition, users’ activities can be monitored. However, for dementia patients, activity recognition alone cannot address the challenges associated with changes in the user’s habits along the disease’s stage transitions. Extending activity recognition to habit recognition enables the capturing of patients’ habits and change sin habits in order to detect anomalies. This paper aims to introduce relevant features for habit recognition solutions, extracted from data, in order to enrich the representation of the user’s habits. This solution is personalisable to meet the specific needs of the patients and generalizable for use in different scenarios. In this way caregivers are better informed on the expected changes of the patient’s habits, which can help to mitigate further deterioration through early treatment and intervention.

  • 49.
    Chimamiwa, Gibson
    et al.
    Örebro University, School of Science and Technology.
    Alirezaie, Marjan
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Multi-sensor dataset of human activities in a smart home environment2021In: Data in Brief, E-ISSN 2352-3409, Vol. 34, article id 106632Article in journal (Refereed)
    Abstract [en]

    Time series data acquired from sensors deployed in smart homes present valuable information for intelligent systems to learn activity patterns of occupants. With the increasing need to enable people to age in place independently, the availability of such data is key to the development of home monitoring solutions. In this article we describe an unlabelled dataset of measurements collected from multiple environmental sensors placed in a smart home to capture human activities of daily living. Various sensors were used including passive infrared, force sensing resistors, reed switches, mini photocell light sensors, temperature and humidity, and smart plugs. The sensors record data from the user's interactions with the environment, such as indoor movements, pressure applied on the bed, or current consumption when using electrical appliances. Millions of raw sensor data samples were collected continuously at a frequency of 1 Hz over a period of six months between 26 February 2020 and 26 August 2020. The dataset can be useful in the analysis of different methods, including data-driven algorithms for activity or habit recognition. In particular, the research community might be interested in investigating the performance of algorithms when applied on unlabelled datasets and not necessarily on annotated datasets. Furthermore, by applying artificial intelligence (AI) algorithms on such data collected over long periods, it is possible to extract patterns that reveal the user's habits as well as detect changes in the habits. This can benefit in detecting deviations in order to provide timely interventions for patients, e.g., people with dementia.

  • 50.
    Chimamiwa, Gibson
    et al.
    Örebro University, School of Science and Technology.
    Giaretta, Alberto
    Örebro University, School of Science and Technology.
    Alirezaie, Marjan
    Örebro University, School of Science and Technology.
    Pecora, Federico
    Örebro University, School of Science and Technology.
    Loutfi, Amy
    Örebro University, School of Science and Technology.
    Are Smart Homes Adequate for Older Adults with Dementia?2022In: Sensors, E-ISSN 1424-8220, Vol. 22, no 11, article id 4254Article, review/survey (Refereed)
    Abstract [en]

    Smart home technologies can enable older adults, including those with dementia, to live more independently in their homes for a longer time. Activity recognition, in combination with anomaly detection, has shown the potential to recognise users' daily activities and detect deviations. However, activity recognition and anomaly detection are not sufficient, as they lack the capacity to capture the progression of patients' habits across the different stages of dementia. To achieve this, smart homes should be enabled to recognise patients' habits and changes in habits, including the loss of some habits. In this study, we first present an overview of the stages that characterise dementia, alongside real-world personas that depict users' behaviours at each stage. Then, we survey the state of the art on activity recognition in smart homes for older adults with dementia, including the literature that combines activity recognition and anomaly detection. We categorise the literature based on goals, stages of dementia, and targeted users. Finally, we justify the necessity for habit recognition in smart homes for older adults with dementia, and we discuss the research challenges related to its implementation.

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