LMNN metric learning and fuzzy nearest neighbour classifier for hand gesture recognition

This paper presents a novel gesture recognition system using a single three-axis accelerometer, that is to serve as an alternative or supplementary interaction modality for controlling mobile devices. Capturing, training and classification of the detected hand gestures are expected to be executed in their entirety on the mobile device running the proposed system, instead of being passed to a nearby computer. As gesture recognition belongs to the group of pattern recognition problems where the underlying class probabilities are not a priori known, the classification is based on the distance between neighbouring examples. The distance metric is optimized by using large margin nearest neighbour (LMNN) method. To measure the amount of classification confidence, a fuzzy version of nearest neighbour algorithm is employed. Obtained results for recognition of nine hand gestures using proposed LMNN—fuzzy combination are presented and compared to that of other similar approaches. The system achieves near perfect recognition accuracy that is highly competitive with systems based on statistical methods and other accelerometer-based gesture recognition systems in the literature.