Skill assistance for myoelectric control using an event-driven task model

Electromyogram (EMG) has often been used as a control signal for a prosthetic arm, which includes information on the operator's motor intentions and the mechanical impedance of joints. Most previous research adopted the control methods of the prosthetic arms based on the EMG pattern discrimination and/or the force estimation from the EMG signals, and did not utilize any knowledge on tasks performed by amputees such as object grasping and soup-spooning tasks. In this paper, a new myoelectric control method is proposed using a statistically organized neural network and an event-driven task model. The task model is represented using a Petri net to describe the task dependent knowledge, which is used to modify the neural network's output. Experimental results show that the use of the task model significantly improves the accuracy of the EMG pattern discrimination.