Accelerometer-based gesture recognition via dynamic-time warping, affinity propagation, & compressive sensing

We propose a gesture recognition system based primarily on a single 3-axis accelerometer. The system employs dynamic time warping and affinity propagation algorithms for training and utilizes the sparse nature of the gesture sequence by implementing compressive sensing for gesture recognition. A dictionary of 18 gestures or classes is defined and a database of over 3,700 repetitions is created from 7 users. Our dictionary of gestures is the largest in published studies related to acceleration-based gesture recognition, to the best of our knowledge. The proposed system achieves almost perfect user-dependent recognition and a userindependent recognition accuracy that is competitive with the statistical methods that require significantly a large number of training samples and with the other accelerometer-based gesture recognition systems available in literature.