Exploiting accelerometers to improve movement classification for prosthetics

Abstract

Recent studies have explored the integration of additional input modalities to improve myoelectric control of prostheses. Arm dynamics in particular are an interesting option, as these can be measured easily by means of accelerometers. In this work, the benefit of accelerometer signals is demonstrated on a large scale movement classification task, consisting of 40 hand and wrist movements obtained from 20 subjects. The results demonstrate that the accelerometer modality is indeed highly informative and even outperforms surface electromyography in terms of classification accuracy. The highest accuracy, however, is obtained when both modalities are integrated in a multi-modal classifier.