Classifying continuous hand grips and movements using myoelectric and accelerometer signals

Gene Shuman, Zoran Duric, Lynn H. Gerber

Abstract: Activities of Daily Living (ADLs) are personal functional activities performed by individuals to carry out their daily lives, allowing them to live independently. Finding relationships between surface electromyograms (sEMG) measured in the arm and movements of the hand and wrist needed to perform ADLs can help address performance deficits and be exploited in designing myoelectric control systems. This paper reports on applying machine learning techniques to discover the electromyogram patterns present when using the hand to perform 47 typical fine motor functional activities used to accomplish ADLs. A Hidden Markov Model (HMM) combined with Random Forest (RF) classification is employed to learn the patterns needed to identify 10 second segments of continuous movement. The HMM/RF model was applied using two feature sets: one consisting entirely of sEMG signals, the other adding accelerometer data. Results show an accuracy improvement of the HMM/RF model over RF-only classification, and further improvement using the combined sEMG+ACC features, with a range of 74.99% to 84.09% and average of 79.06% for five subjects.