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.
Citation
Proceedings of the MEC'11 conference, UNB; 2011.
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