Estimation of hand grasp force based on forearm surface EMG

In the force control of multi-functional prosthetic hands, it is important to extract grasp force information besides mode specifications directly from the myoelectric signals. In this paper, a force sensor is adopted to record the hand's enveloping force when the hand is performing several grasp modes, synchronously with 6 channels surface electromyography (EMG) which are extracting from the subject's forearm. Three pattern regression methods, locally weighted projection regression (LWPR), artificial neural network (ANN) and support vector machine (SVM) are used to find the best representative relationship of these two kinds of signals. Experimental results show that the SVM method is better than LWPR and ANN, especially in the case of cross-session validation. Also, the force regression performance is better when grasping within several specific modes than grasping randomly. Based on these results, an efficient online prediction of the hand grasp force is present finally, with an accuracy of around 0.9 in squared correlation coefficient (SCC) and 5~10 N error over a range of 60 N. It can be utilized for the prosthetic hand's control to provide a reasonable exerting force reference.