Prosthesis-Guided Training For Practical Use Of Pattern Recognition Control Of Prostheses

Abstract

The potential for pattern recognition to improve powered prosthesis control has been discussed for many years. One remaining barrier to at-home use of these techniques is that practical methods of user prompting during system training are lacking. Most research and development of pattern recognition systems for prosthesis control has relied on on-screen cues to prompt the prosthesis wearer during signal collection; therefore most systems require connection to a computer or external device. We have developed a method called Prosthesis-Guided Training (PGT) to address this issue. In PGT, the prosthesis itself moves through a pre-programmed sequence of motions to prompt the wearer to elicit the appropriate muscle contractions. PGT requires no extra hardware and allows wearers to retrain, refresh, or recalibrate the controller in many locations and situations. Training via PGT is self-initiated and requires only about 1 minute of the wearer’s time. Furthermore, PGT provides a practical mechanism for overcoming malfunctioning or changing inputs, addresses differences in routine donning, and results in acquisition of myoelectric signals representative of those elicited during functional use. Qualitative and quantitative data acquired to investigate the efficacy of PGT suggest that it is an intuitive, effective, and clinically viable method of training pattern recognition–controlled prostheses.