SVM for Estimation of Wrist Angle from Sonomyography And SEMG Signals

The skeletal muscle plays a very important role in the human movement. Surface electromyography (SEMG) is a very useful tool for the functional assessment of skeletal muscles, while sonography has been commonly used to detect its morphological information. We named the signal about the muscle morphological changes derived from ultrasound as sonomyography (SMG). In this study, the ultrasound image, SEMG signals were synchronously sampled from the extensor carpi radialis muscle together with the wrist angle during the whole process of wrist extension and flexion. A Support Vector Machine (SVM) algorithm was used to estimate the wrist angle with the muscle deformation SMG and root mean square of SEMG signals as inputs. The overall mean correlation coefficient value was 0.96 plusmn 0.02, the mean standard root mean square error was 7.26 plusmn 1.98, and the root mean square difference was 0.16 plusmn 0.03. The results demonstrated that the wrist angle could be well estimated by combining the SMG and SEMG signals with SVM algorithm. The combination of SMG and SEMG could provide more comprehensive information to study skeletal muscle.