PROJECT TITLE :

An Analysis of Intrinsic and Extrinsic Hand Muscle EMG for Improved Pattern Recognition Control

ABSTRACT:

Pattern recognition control combined with surface electromyography (EMG) from the extrinsic hand muscles has shown great promise for management of multiple prosthetic functions for transradial amputees. There's, but, a would like to adapt this management technique when implemented for partial-hand amputees, who possess each a functional wrist and information-made residual intrinsic hand muscles. We demonstrate that combining EMG data from both intrinsic and extrinsic hand muscles to classify hand grasps and finger motions allows up to 19 categories of hand grasps and individual finger motions to be decoded, with an accuracy of 96% for non-amputees and eighty fivep.c for partial-hand amputees. We tend to evaluated real-time pattern recognition management of 3 hand motions in seven completely different wrist positions. We have a tendency to found that a system trained with both intrinsic and extrinsic muscle EMG data, collected whereas statically and dynamically varying wrist position increased completion rates from seventy threep.c to 96p.c for partial-hand amputees and from eighty eight% to a hundredpercent for non-amputees in comparison to a system trained with only extrinsic muscle EMG data collected in a very neutral wrist position. Our study shows that incorporating intrinsic muscle EMG data and wrist motion will considerably improve the robustness of pattern recognition control for application to partial-hand prosthetic management.