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  • 1.
    Li, Cai
    et al.
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Bredies, Katharina
    Department of Design, Faculty of Textiles, Engineering and Business, University of Borås, Sweden.
    Lund, Anja
    Department of Textile Technology, Faculty of Textiles, Engineering and Business, University of Borås, Sweden.
    Nierstrasz, Vincent
    Department of Textile Technology, Faculty of Textiles, Engineering and Business, University of Borås, Sweden.
    Hemeren, Paul
    University of Skövde, School of Informatics. University of Skövde, The Informatics Research Centre.
    Högberg, Dan
    University of Skövde, School of Engineering Science. University of Skövde, The Virtual Systems Research Centre.
    k-Nearest-Neighbour based Numerical Hand Posture Recognition using a Smart Textile Glove2015In: AMBIENT 2015: The Fifth International Conference on Ambient Computing, Applications, Services and Technologies / [ed] MaartenWeyn, International Academy, Research and Industry Association (IARIA), 2015, p. 36-41Conference paper (Refereed)
    Abstract [en]

    In this article, the authors present an interdisciplinary project that illustrates the potential and challenges in dealing with electronic textiles as sensing devices. An interactive system consisting of a knitted sensor glove and electronic circuit and a numeric hand posture recognition algorithm based on k-nearestneighbour (kNN) is introduced. The design of the sensor glove itself is described, considering two sensitive fiber materials – piezoresistive and piezoelectric fibers – and the construction using an industrial knitting machine as well as the electronic setup is sketched out. Based on the characteristics of the textile sensors, a kNN technique based on a condensed dataset has been chosen to recognize hand postures indicating numbers from one to five from the sensor data. The authors describe two types of data condensation techniques (Reduced Nearest Neighbours and Fast Condensed Nearest Neighbours) in order to improve the data quality used by kNN, which are compared in terms of run time, condensation rate and recognition accuracy. Finally, the article gives an outlook on potential application scenarios for sensor gloves in pervasive computing.

  • 2.
    Syrén, Felicia
    et al.
    Textile Materials Technology, Department of Textile Technology, Faculty of Textiles, Engineering and Business, University of Borås, Borås, Sweden.
    Li, Cai
    University of Skövde, School of Informatics.
    Billing, Erik
    University of Skövde, School of Informatics.
    Lund, Anja
    Textile Materials Technology, Department of Textile Technology, Faculty of Textiles, Engineering and Business, University of Borås, Borås, Sweden.
    Nierstrasz, Vincent
    Textile Materials Technology, Department of Textile Technology, Faculty of Textiles, Engineering and Business, University of Borås, Borås, Sweden.
    Characterization of textile resistive strain sensors2016Conference paper (Other academic)
1 - 2 of 2
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