Abstract
Hand gesture can express rich information. However, existing hand shape recognition methods have several problems. In order to utilize hand gesture in a home automation, we have focused on “wrist contour”, and have developed a wrist-watch-type device that measures wrist contour using photo reflector arrays. In this paper, we try on two challenges: the first is improvement of the hand shape recognition performance, and the second is making clear the effect of personal difference and finding a key to overcome the difference. We collect wrist contour data from 28 subjects and conduct two kinds of experiments. As for the first challenge, three different feature types are compared. The experimental results extract several important contour statistics and the classification rate itself is also improved by introducing multiple subjects’ data for training. As for the second challenge, we compose a resemblance matrix to evaluate resemblance among subjects. The results indicate that training data selection is important to improve the classification performance, especially when we don’t have time to collect enough training data for a new user.
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Fukui, R., Watanabe, M., Shimosaka, M., Sato, T. (2013). Hand Shape Classification with a Wrist Contour Sensor. In: Desai, J., Dudek, G., Khatib, O., Kumar, V. (eds) Experimental Robotics. Springer Tracts in Advanced Robotics, vol 88. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-00065-7_62
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DOI: https://doi.org/10.1007/978-3-319-00065-7_62
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