Algorithm based on continuous data stream for dynamic gesture recognition

For the purpose of recognizing the sequence of dynamic gesture made by operator, a method was presented based on continuous data streams sampled from data glove, which used singular value decomposition (SVD) to eliminating noise and extracting features. The characteristics of physiology about joint bend was applied making user-dependent information be culled. A set of gesture template which across different users was set up. The template which gives a complete description of gesture's feature and generalizes it is therefore user-independent. Based on Hill Climbing heuristic, these streams were separated into action sequences, then a similarity measurement using Euclidian distance was adopted in real time between all segments and templates on a hierarchy search tree built in advance. The sequences segmented by this method are accuracy and suitable for multi users. The effectiveness of this approach for identifying dynamic gesture was verified by two empirical experiments which using 5DT data glove.