Improving myoelectric pattern recognition positional robustness using advanced training protocols

The control of powered upper limb prostheses using the surface electromyogram (EMG) is an important clinical option for amputees. There have been considerable recent improvements in prosthetic hands, but these currently lack a control scheme that can decode movement intent from the EMG to exploit their mechanical dexterity. Pattern recognition based control has the potential to decode many classes of movement intent, but is confounded when using the prosthesis in varying positions during activities of daily living. This work describes the degradation that can occur when using pattern recognition in varying positions, during both static positioning tasks and dynamic activities of daily living. It is shown that training with dynamic activities can greatly improve positional robustness for both static and dynamic tasks, without requiring a complex and lengthy training session.