Abstract
Gesture recognition is important, because it is a useful communication medium between humans and computers. In this paper we use multiple sensors, i.e., PSD cameras for detecting LEDs attached on a body and DataGloves for both hands. One of the major difficulties in gesture recognition is temporal segmentation from continuous motion. We use training samples which are manually segmented and labeled as prior knowledge. A self-organizing map(SOM) is constructed based on training samples. Test gestural data are segmented by systematic search to obtain the best match with reference vectors on a competitive layer. A comparative study is done between the use of a single SOM and 3 SOMs for representing spatio-temporal information obtained from PSD cameras and DataGloves.
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Ishikawa, M. (2004). Gesture Recognition Based on SOM Using Multiple Sensors. In: Rajapakse, J.C., Wang, L. (eds) Neural Information Processing: Research and Development. Studies in Fuzziness and Soft Computing, vol 152. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39935-3_21
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DOI: https://doi.org/10.1007/978-3-540-39935-3_21
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