Simplification of gesture modeling by boundary analysis in active signals from wearable data glove

The implicit assistive communication by means of gestures is highly appreciated in the fields of assistive technology and security. The reason for such technique is to extract the communications in terms of intensions from the disabled community. Such systems play a very crucial role during emergency as they perform the communication during normal course of life. To interpret and communicate most distinctive requirements to the caregivers or medical support agents, a well defined and distinguishable gesture paradigm and its recognition is necessary. Conversion of communicative gestures is to be made precise and easy. The proposed method of feature construction made a simple modeling of the signals with the consideration of time zones separated during the gesture. The identification of most active channels during the time of gesture and use them reduces complexity in processing and hardware cost. Repeated trials were used to test this modeling for different activity gestures and the results were found identical and forming a pattern. The signals acquired from a set of four different gestures with six trials for each.