TY - JOUR
T1 - Multiple binary classifications via linear discriminant analysis for improved controllability of a powered prosthesis
AU - Hargrove, Levi J.
AU - Scheme, Erik J.
AU - Englehart, Kevin B.
AU - Hudgins, Bernard S.
N1 - Funding Information:
Manuscript received December 03, 2008; revised August 05, 2009; accepted October 01, 2009. First published January 12, 2010; current version published February 24, 2010. This work was supported in part by NSERC Discovery Grants 171368-03 and 217354-01, in part by the New Brunswick Innovation Foundation, and in part by the Atlantic Innovation Fund L. Hargrove was with the Institute of Biomedical Engineering, University of New Brunswick, Fredericton, NB, E3B 5A3, Canada. He is now with the Neural Engineering Center for Artificial Limbs, Rehabilitation Institute of Chicago, Chicago, IL 60611 USA, and with the Department of Physical Medicine and Rehabilitation at the Feinberg School of Medicine, Northwestern University, Chicago, IL 60611 USA (e-mail: [email protected]).
PY - 2010/2
Y1 - 2010/2
N2 - This paper describes a novel pattern recognition based myoelectric control system that uses parallel binary classification and class specific thresholds. The system was designed with an intuitive configuration interface, similar to existing conventional myoelectric control systems. The system was assessed quantitatively with a classification error metric and functionally with a clothespin test implemented in a virtual environment. For each case, the proposed system was compared to a state-of-the-art pattern recognition system based on linear discriminant analysis and a conventional myoelectric control scheme with mode switching. These assessments showed that the proposed control system had a higher classification error (p < 0.001) but yielded a more controllable myoelectric control system (p < 0.001) as measured through a clothespin usability test implemented in a virtual environment. Furthermore, the system was computationally simple and applicable for real-time embedded implementation. This work provides the basis for a clinically viable pattern recognition based myoelectric control system which is robust, easily configured, and highly usable.
AB - This paper describes a novel pattern recognition based myoelectric control system that uses parallel binary classification and class specific thresholds. The system was designed with an intuitive configuration interface, similar to existing conventional myoelectric control systems. The system was assessed quantitatively with a classification error metric and functionally with a clothespin test implemented in a virtual environment. For each case, the proposed system was compared to a state-of-the-art pattern recognition system based on linear discriminant analysis and a conventional myoelectric control scheme with mode switching. These assessments showed that the proposed control system had a higher classification error (p < 0.001) but yielded a more controllable myoelectric control system (p < 0.001) as measured through a clothespin usability test implemented in a virtual environment. Furthermore, the system was computationally simple and applicable for real-time embedded implementation. This work provides the basis for a clinically viable pattern recognition based myoelectric control system which is robust, easily configured, and highly usable.
KW - Artificial arms
KW - Electromyogram (EMG)
KW - Myoelectric signal (MES)
KW - Myoelectric signals
KW - Pattern recognition
KW - Powered prostheses
UR - http://www.scopus.com/inward/record.url?scp=77649305658&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=77649305658&partnerID=8YFLogxK
U2 - 10.1109/TNSRE.2009.2039590
DO - 10.1109/TNSRE.2009.2039590
M3 - Article
C2 - 20071277
AN - SCOPUS:77649305658
SN - 1534-4320
VL - 18
SP - 49
EP - 57
JO - IEEE Transactions on Neural Systems and Rehabilitation Engineering
JF - IEEE Transactions on Neural Systems and Rehabilitation Engineering
IS - 1
M1 - 5378611
ER -