TY - GEN
T1 - Motion Pattern Recognition of Lower Limb Exoskeleton Based on SAPSO-SVM
AU - Liang, Zhanbo
AU - Liu, Yali
AU - Song, Qiuzhi
AU - Wu, Dehao
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Accurate motion pattern recognition is the key to achieving human-computer cooperative control of the lower limb exoskeleton. This paper puts forward a motion pattern recognition method of lower limb exoskeleton based on an optimized support vector machine (SAPSO-SVM) via inertial sensor. This method introduces simulated annealing (SA) mechanism into particle swarm optimization (PSO) algorithm, which solves the problem that PSO algorithm is easy to converge locally to a certain extent, so as to acquire better parameters of classification model. Based on the RMS feature values of joint angle signals, we classified and recognized different lower limb motion patterns. The results reveal that the average recognition accuracy of SAPSO-SVM in single motion pattern is approximately 96.93%, and the Kappa coefficient is 0.9617, which has excellent consistency. The SAPSO-SVM method can further improve the effect of lower limb exoskeleton motion pattern recognition, and has good application value.
AB - Accurate motion pattern recognition is the key to achieving human-computer cooperative control of the lower limb exoskeleton. This paper puts forward a motion pattern recognition method of lower limb exoskeleton based on an optimized support vector machine (SAPSO-SVM) via inertial sensor. This method introduces simulated annealing (SA) mechanism into particle swarm optimization (PSO) algorithm, which solves the problem that PSO algorithm is easy to converge locally to a certain extent, so as to acquire better parameters of classification model. Based on the RMS feature values of joint angle signals, we classified and recognized different lower limb motion patterns. The results reveal that the average recognition accuracy of SAPSO-SVM in single motion pattern is approximately 96.93%, and the Kappa coefficient is 0.9617, which has excellent consistency. The SAPSO-SVM method can further improve the effect of lower limb exoskeleton motion pattern recognition, and has good application value.
KW - SAPSO-SVM
KW - lower limb exoskeleton
KW - motion pattern recognition
UR - http://www.scopus.com/inward/record.url?scp=85160005914&partnerID=8YFLogxK
U2 - 10.1109/ISAIAM55748.2022.00034
DO - 10.1109/ISAIAM55748.2022.00034
M3 - Conference contribution
AN - SCOPUS:85160005914
T3 - Proceedings - 2022 2nd International Symposium on Artificial Intelligence and its Application on Media, ISAIAM 2022
SP - 140
EP - 146
BT - Proceedings - 2022 2nd International Symposium on Artificial Intelligence and its Application on Media, ISAIAM 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2nd International Symposium on Artificial Intelligence and its Application on Media, ISAIAM 2022
Y2 - 10 June 2022 through 12 June 2022
ER -