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.