Hand motion recognition using a distance sensor array
Sung-Gwi Cho, Masahiro Yoshikawa, Ming Ding, Jun Takamatsu, Tsukasa Ogasawara · 2017
Many studies of hand motion recognition using a surface electromyogram (sEMG) have been conducted. However, it is difficult to get the activity of deep layer muscles from an sEMG. The pronation and supination of the forearm are caused by the activities of deep layer muscles. These motions are important in grasping and manipulating daily objects. We think it is possible to accurately recognize hand motions from the activity of the deep layer muscles using the forearm deformation. Forearm deformation is caused by a complex motion of the surface and deep layer muscles, tendons, and bones. In this study, we propose a novel hand motion recognition method based on measuring forearm deformation with a distance sensor array. The distance sensor array is designed based on a 3D model of the forearm. It can measure small deformations because the shape of the array is designed to fit the neutral position of the forearm. A Support Vector Machine (SVM) is used to recognize seven types of hand motion. Two types of features are extracted for the recognition based on the time difference of the forearm deformation. Using the proposed method, we perform hand motion recognition experiments. The experimental results showed that the proposed method correctly recognized hand motions caused by the activity of both surface and deep layer muscles, including the pronation and supination of the forearm. Moreover, the hand opening of small deformation motions was correctly recognized.