Abstract:
© 2020 SPIE. Hand gesture recognition algorithms require information from the material world to be converted to digital data. In this paper we present an analysis of dielectric elastomer sensors for hand gesture recognition. A glove with five dielectric elastomer sensors has been used to collect motion data from the hand. The capacitance value of each sensor was read and analysed for a total of 24 participants. The study shows that the sensors provide enough information to differentiate gestures from each participant, although the maximum capacitance value varied with each participant, making gesture recognition over all participants difficult. Data processing allowed for this problem to be solved.
