Motion recognition from contact force measurement

Optical motion capture systems, which are used in broad fields of research, are costly; they need large installation space and calibrations. We find difficulty in applying it in typical homes and care centers. Therefore we propose to use low cost contact force measurement systems to develop rehabilitation and healthcare monitoring tools. Here, we propose a novel algorithm for motion recognition using the feature vector from force data solely obtained during a daily exercise program. We recognized 7 types of movement (Radio Exercises) of two candidates (mean age 22, male). The results show that the recognition rate of each motion has high score (mean: 86.9%). The results also confirm that there is a clustering of each movement in personal exercises data, and a similarity of the clustering even for different candidates thus that motion recognition is possible using contact force data.