Gesture recognition method with acceleration data weighted by SEMG

Abstract

In this paper, we propose a gesture recognition method with acceleration data weighted by sEMG. Acceleration and sEMG are collected as training data, and gestures are recognized using only acceleration as input data. The dynamic time warping (DTW) algorithm is used for the distance calculation. Three axis acceleration data and sEMG were collected for three types of baseball pitching forms: overarm, sidearm, and underarm beginning from three types of preliminary motions: no windup, quick, and windup. We investigate the changes in sEMG during pitching motion. The distance calculation method is changed according to the sEMG amplitude, reducing the influence of unstable motions. In evaluation experiments, the proposed method achieved higher accuracy than the comparison method that does not use sEMG for windup form, even if the number of training data changes.