Recognition of finger/hand grip mechanism by computing S-transform features of surface electromyogram signal from healthy and amputee

Accurate identification of intended grip actions using the myoelectric signal recorded from the surface of the residual muscles can facilitate natural control of a prosthetic hand for an amputee. However, this is not trivial due to the complexity of the hand muscles. To overcome these shortcomings, there is the need for determining features of the myoelectric recordings that can be used for accurate identification of the grip actions. This study reports the use of S-transform (ST) of the surface myoelectric recordings for recognizing the intent of the user to generate a set of grip patterns. Surface Electromyogram (sEMG) recorded while performing five different hand/finger grip patterns was analyzed. ST of the signal was computed to analyze the signal in a windowed time-frequency domain. The energy and mean amplitude of the transformed signal were classified using a neural network. The method was tested for able-hand and trans-radial amputee subjects. The results show that ST showed improved sensitivity, specificity and accuracy for both healthy and amputee people.