TY - GEN
T1 - Powered prosthesis control during walking, sitting, standing, and non-weight bearing activities using neural and mechanical inputs
AU - Simon, Ann M.
AU - Fey, Nicholas P.
AU - Ingraham, Kimberly A.
AU - Young, Aaron J.
AU - Hargrove, Levi J.
PY - 2013/12/1
Y1 - 2013/12/1
N2 - Lower limb prostheses that can generate near physiological joint power have the potential to improve the way amputees go about their activities of daily living. Amputees who have lost both their knee and ankle would also benefit from a system that allowed them to easily perform sit-to-stand and stand-to-sit movements, reposition their prosthesis using neural control, and intuitively transition between these modes of operation and walking. In this study, we developed such a system and evaluated it with two transfemoral amputees. Both amputees were able to stand up and sit down comfortably using the powered prosthesis. Two neural control systems were configured using a linear discriminant analysis classifier trained from data recorded from eight residual thigh muscles. One classifier, trained to recognize when amputees sat down from walking mode, was on average 96.5% accurate. A second classifier, trained to recognize amputees' intent to reposition the knee and ankle joints, was on average 87.3% accurate. This integrated control system allowing transfemoral amputees to walk as well as perform weight transfers (sitting down and standing up) and seated non-weight bearing activities demonstrates an advancement towards improving the performance and viability of powered prostheses during daily use.
AB - Lower limb prostheses that can generate near physiological joint power have the potential to improve the way amputees go about their activities of daily living. Amputees who have lost both their knee and ankle would also benefit from a system that allowed them to easily perform sit-to-stand and stand-to-sit movements, reposition their prosthesis using neural control, and intuitively transition between these modes of operation and walking. In this study, we developed such a system and evaluated it with two transfemoral amputees. Both amputees were able to stand up and sit down comfortably using the powered prosthesis. Two neural control systems were configured using a linear discriminant analysis classifier trained from data recorded from eight residual thigh muscles. One classifier, trained to recognize when amputees sat down from walking mode, was on average 96.5% accurate. A second classifier, trained to recognize amputees' intent to reposition the knee and ankle joints, was on average 87.3% accurate. This integrated control system allowing transfemoral amputees to walk as well as perform weight transfers (sitting down and standing up) and seated non-weight bearing activities demonstrates an advancement towards improving the performance and viability of powered prostheses during daily use.
KW - Biomechanics
KW - Mechanically active prosthesis
KW - Neural intent recognition
KW - Prosthesis control
KW - Transfemoral amputation
UR - http://www.scopus.com/inward/record.url?scp=84897687684&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84897687684&partnerID=8YFLogxK
U2 - 10.1109/NER.2013.6696148
DO - 10.1109/NER.2013.6696148
M3 - Conference contribution
AN - SCOPUS:84897687684
SN - 9781467319690
T3 - International IEEE/EMBS Conference on Neural Engineering, NER
SP - 1174
EP - 1177
BT - 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
T2 - 2013 6th International IEEE EMBS Conference on Neural Engineering, NER 2013
Y2 - 6 November 2013 through 8 November 2013
ER -