TY - GEN
T1 - Different sEMG and EEG Features Analysis for Gait phase Recognition
AU - Wei, Pengna
AU - Zhang, Jinhua
AU - Wei, Pingping
AU - Wang, Baozeng
AU - Hong, Jun
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/7
Y1 - 2020/7
N2 - This research focuses on the gait phase recognition using different sEMG and EEG features. Seven healthy volunteers, 23-26 years old, were enrolled in this experiment. Seven phases of gait were divided by three-dimensional trajectory of lower limbs during treadmill walking and classified by Library for Support Vector Machines (LIBSVM). These gait phases include loading response, mid-stance, terminal Stance, pre-swing, initial swing, mid-swing, and terminal swing. Different sEMG and EEG features were assessed in this study. Gait phases of three kinds of walking speed were analyzed. Results showed that the slope sign change (SSC) and mean power frequency (MPF) of sEMG signals and SSC of EEG signals achieved higher accuracy of gait phase recognition than other features, and the accuracy are 95.58% (1.4 km/h), 97.63% (2.0 km/h) and 98.10% (2.6 km/h) respectively. Furthermore, the accuracy of gait phase recognition in the speed of 2.6 km/h is better than other walking speeds.
AB - This research focuses on the gait phase recognition using different sEMG and EEG features. Seven healthy volunteers, 23-26 years old, were enrolled in this experiment. Seven phases of gait were divided by three-dimensional trajectory of lower limbs during treadmill walking and classified by Library for Support Vector Machines (LIBSVM). These gait phases include loading response, mid-stance, terminal Stance, pre-swing, initial swing, mid-swing, and terminal swing. Different sEMG and EEG features were assessed in this study. Gait phases of three kinds of walking speed were analyzed. Results showed that the slope sign change (SSC) and mean power frequency (MPF) of sEMG signals and SSC of EEG signals achieved higher accuracy of gait phase recognition than other features, and the accuracy are 95.58% (1.4 km/h), 97.63% (2.0 km/h) and 98.10% (2.6 km/h) respectively. Furthermore, the accuracy of gait phase recognition in the speed of 2.6 km/h is better than other walking speeds.
KW - Electroencephalogram (EEG)
KW - Feature
KW - Gait phase recognition
KW - Surface Electromyogram (sEMG)
UR - https://www.scopus.com/pages/publications/85091046203
U2 - 10.1109/EMBC44109.2020.9175655
DO - 10.1109/EMBC44109.2020.9175655
M3 - 会议稿件
AN - SCOPUS:85091046203
T3 - Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS
SP - 1002
EP - 1006
BT - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020
Y2 - 20 July 2020 through 24 July 2020
ER -