TY - JOUR
T1 - A strategy for identifying locomotion modes using surface electromyography
AU - Huang, He
AU - Kuiken, Todd A.
AU - Lipschutz, Robert D.
N1 - Funding Information:
Manuscript received November 2, 2007; revised June 19, 2008. First published August 12, 2008; current version published February 13, 2009. This work was supported in part by the Globe Foundation, in part by the National Institutes of Health (NIH) National Institute of Child and Human Development under Grant R01 HD043137, and in part by the National Institute on Disability and Rehabilitation Research, U.S. Department of Education, under Grant H133F080006. Asterisk indicates corresponding author.
PY - 2009/1
Y1 - 2009/1
N2 - This study investigated the use of surface electromyography (EMG) combined with pattern recognition (PR) to identify user locomotion modes. Due to the nonstationary characteristics of leg EMG signals during locomotion, a new phase-dependent EMG PR strategy was proposed for classifying the user's locomotion modes. The variables of the system were studied for accurate classification and timely system response. The developed PR system was tested on EMG data collected from eight able-bodied subjects and two subjects with long transfemoral (TF) amputations while they were walking on different terrains or paths. The results showed reliable classification for the seven tested modes. For eight able-bodied subjects, the average classification errors in the four defined phases using ten electrodes located over the muscles above the knee (simulating EMG from the residual limb of a TF amputee) were 12.4% ± 5.0%, 6.0% ± 4.7%, 7.5% ± 5.1%, and 5.2% ± 3.7%, respectively. Comparable results were also observed in our pilot study on the subjects with TF amputations. The outcome of this investigation could promote the future design of neural-controlled artificial legs.
AB - This study investigated the use of surface electromyography (EMG) combined with pattern recognition (PR) to identify user locomotion modes. Due to the nonstationary characteristics of leg EMG signals during locomotion, a new phase-dependent EMG PR strategy was proposed for classifying the user's locomotion modes. The variables of the system were studied for accurate classification and timely system response. The developed PR system was tested on EMG data collected from eight able-bodied subjects and two subjects with long transfemoral (TF) amputations while they were walking on different terrains or paths. The results showed reliable classification for the seven tested modes. For eight able-bodied subjects, the average classification errors in the four defined phases using ten electrodes located over the muscles above the knee (simulating EMG from the residual limb of a TF amputee) were 12.4% ± 5.0%, 6.0% ± 4.7%, 7.5% ± 5.1%, and 5.2% ± 3.7%, respectively. Comparable results were also observed in our pilot study on the subjects with TF amputations. The outcome of this investigation could promote the future design of neural-controlled artificial legs.
KW - Electromyography (EMG)
KW - Neural-machine interface
KW - Pattern recognition (PR)
KW - Prosthesis
KW - Targeted muscle reinnervation
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U2 - 10.1109/TBME.2008.2003293
DO - 10.1109/TBME.2008.2003293
M3 - Article
C2 - 19224720
AN - SCOPUS:60549118041
SN - 0018-9294
VL - 56
SP - 65
EP - 73
JO - IRE transactions on medical electronics
JF - IRE transactions on medical electronics
IS - 1
ER -