Towards improved partial-hand prostheses: The effect of wrist kinematics on pattern-recognition-based control

Pattern-recognition-based control using surface electromyography (EMG) from the extrinsic hand muscles has shown great promise for providing control of multiple prosthetic functions. However, it is not clear how these systems will perform when the user possesses a functional wrist; an attribute unique to the population of partial-hand amputees. Fortunately, partial-hand amputees may have remaining intrinsic hand muscles, from which additional information-rich EMG data may be extracted and used for prosthetic control. We investigated the effect of statically and dynamically varying wrist position on a pattern recognition system´s ability to classify hand grasp patterns in able-bodied individuals. We found that varying wrist position significantly degraded the system´s performance (p<;0.001). The system performed worse when trained only with EMG data from the extrinsic hand muscles than when trained with EMG data from the intrinsic hand muscles. The system´s performance significantly improved when trained in all static wrist positions (p<;0.001) and with all dynamic wrist motions (p<;0.001).