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  • 1.
    Bevilacqua, Fernando
    et al.
    Computer Science, Federal University of Fronteira Sul, Chapecó 89802 112, Brazil.
    Engström, Henrik
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Backlund, Per
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Game-Calibrated and User-Tailored Remote Detection of Stress and Boredom in Games2019In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 13, p. 1-43, article id 2877Article in journal (Refereed)
    Abstract [en]

    Emotion detection based on computer vision and remote extraction of user signals commonly rely on stimuli where users have a passive role with limited possibilities for interaction or emotional involvement, e.g., images and videos. Predictive models are also trained on a group level, which potentially excludes or dilutes key individualities of users. We present a non-obtrusive, multifactorial, user-tailored emotion detection method based on remotely estimated psychophysiological signals. A neural network learns the emotional profile of a user during the interaction with calibration games, a novel game-based emotion elicitation material designed to induce emotions while accounting for particularities of individuals. We evaluate our method in two experiments (n = 20 and n = 62) with mean classification accuracy of 61.6%, which is statistically significantly better than chance-level classification. Our approach and its evaluation present unique circumstances: our model is trained on one dataset (calibration games) and tested on another (evaluation game), while preserving the natural behavior of subjects and using remote acquisition of signals. Results of this study suggest our method is feasible and an initiative to move away from questionnaires and physical sensors into a non-obtrusive, remote-based solution for detecting emotions in a context involving more naturalistic user behavior and games.

  • 2.
    Olson, Nasrine
    et al.
    Swedish School of Library and Information Science (SSLIS), University of Borås, Borås, Sweden.
    Bae, Juhee
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Biosensors-Publication Trends and Knowledge Domain Visualization2019In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 19, no 11, article id 2615Article in journal (Refereed)
    Abstract [en]

    The number of scholarly publications on the topic of biosensors has increased rapidly; as a result, it is no longer easy to build an informed overview of the developments solely by manual means. Furthermore, with many new research results being continually published, it is useful to form an up-to-date understanding of the recent trends or emergent directions in the field. This paper utilizes bibliometric methods to provide an overview of the developments in the topic based on scholarly publications. The results indicate an increasing interest in the topic of biosensor(s) with newly emerging sub-topics. The US is identified as the country with highest total contribution to this area, but as a collective, EU countries top the list of total contributions. An examination of trends over the years indicates that in recent years, China-based authors have been more productive in this area. If research contribution per capita is considered, Singapore takes the top position, followed by Sweden, Switzerland and Denmark. While the number of publications on biosensors seems to have declined in recent years in the PubMed database, this is not the case in the Web of Science database. However, there remains an indication that the rate of growth in the more recent years is slowing. This paper also presents a comparison of the developments in publications on biosensors with the full set of publications in two of the main journals in the field. In more recent publications, synthetic biology, smartphone, fluorescent biosensor, and point-of-care testing are among the terms that have received more attention. The study also identifies the top authors and journals in the field, and concludes with a summary and suggestions for follow up research.

  • 3.
    Zhou, Bo
    et al.
    German Research Center for Artificial Intelligence, Kaiserslautern, Germany.
    Velez Altamirano, Carlos Andres
    Department Computer Science, University of Kaiserslautern, Kaiserslautern, Germany.
    Cruz Zurian, Heber
    Department Computer Science, University of Kaiserslautern, Kaiserslautern, Germany.
    Atefi, Seyed Reza
    Swedish School of Textiles, University of Borås, Borås, Sweden.
    Billing, Erik
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Seoane Martinez, Fernando
    Swedish School of Textiles, University of Borås, Borås, Sweden / Institute for Clinical Science, Intervention and Technology, Karolinska Institutet, Stockholm, Sweden / Department Biomedical Engineering, Karolinska University Hospital, Stockholm, Sweden.
    Lukowicz, Paul
    German Research Center for Artificial Intelligence, Kaiserslautern, Germany / Department Computer Science, University of Kaiserslautern, Kaiserslautern, Germany.
    Textile Pressure Mapping Sensor for Emotional Touch Detection in Human-Robot Interaction2017In: Sensors, ISSN 1424-8220, E-ISSN 1424-8220, Vol. 17, no 11, article id 2585Article in journal (Refereed)
    Abstract [en]

    In this paper, we developed a fully textile sensing fabric for tactile touch sensing as the robot skin to detect human-robot interactions. The sensor covers a 20-by-20 cm2 area with 400 sensitive points and samples at 50 Hz per point. We defined seven gestures which are inspired by the social and emotional interactions of typical people to people or pet scenarios. We conducted two groups of mutually blinded experiments, involving 29 participants in total. The data processing algorithm first reduces the spatial complexity to frame descriptors, and temporal features are calculated through basic statistical representations and wavelet analysis. Various classifiers are evaluated and the feature calculation algorithms are analyzed in details to determine each stage and segments’ contribution. The best performing feature-classifier combination can recognize the gestures with a 93.3% accuracy from a known group of participants, and 89.1% from strangers.

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