Recognition of a hand-gesture based on self-organization using a DataGlove

We have succeeded in recognizing 32 kinds of hand shapes based on self-organization by measuring the angles of 10 joints of a hand using a DataGlove. Recognition of hand gestures, however, is far more difficult, because it must recognize a sequence of hand shapes instead of its snapshot. An essential difficulty in gesture recognition is how to deal with a sequence of body postures or hand shapes. Since a hand shape is represented by a 10-dimensional (10D) vector measured by a DataGlove, a hand gesture is represented by a sequence of 10D vectors. Our proposal is to recognize a hand gesture by the following procedure. (1) Angles of finger joints are measured at some time interval by a DataGlove. (2) Each gesture is segmented from a sequence of hand shapes. (3) The data length, i.e. the number of snapshots in each gesture, is adjusted to obtain data of a fixed length. (4) An input vector for self-organization is obtained by connecting a sequence of 10D hand-shape vectors. (5) Clustering of hand gestures is carried out by self-organization according to their similarities. Since self-organization is not directly applicable to time series data, the fourth step is the key idea for recognition. We present a detailed description of the proposed recognition method. We then give an overview of hand-gesture data obtained by a DataGlove. Finally, the results of some recognition experiments are provided. This is followed by discussions and conclusions