TY - GEN
T1 - Evaluation of sEMG-Based Feature Extraction and Effective Classification Method for Gait Phase Detection
AU - Peng, Fang
AU - Peng, Wei
AU - Zhang, Cheng
PY - 2019/1/1
Y1 - 2019/1/1
N2 - Gait phase detection is an essential procedure for amputated person with an artificial leg to walk naturally. However, a high-performance gait phase detection system is challenging due to (1) the complexity of surface electromyography (sEMG) and redundancy among the numerous features; (2) a robust recognition algorithm which can satisfy the real-time and high accuracy requirement of the system. This paper presents a gait phase detection method based on feature selection and ensemble learning. Four kinds of features extraction methods of sEMG, including time, frequency, time/frequency domain and entropy, are quantitatively analyzed by statistical analysis and calculation complexity to select the best features set. Furthermore, a multiclass classifier using Light Gradient Boosting Machine (LightGBM) is first introduced in gait recognition for discriminating six different gait phases with an average accuracy (94.1%) in a reasonable calculation time (85Â ms), and the average accuracy is 5%, which is better than the traditional multiple classifiers decision fusion model. The proposed robust algorithm can effectively reduce the effect of speed on the result, which make it a perfect choice for gait phase detection.
AB - Gait phase detection is an essential procedure for amputated person with an artificial leg to walk naturally. However, a high-performance gait phase detection system is challenging due to (1) the complexity of surface electromyography (sEMG) and redundancy among the numerous features; (2) a robust recognition algorithm which can satisfy the real-time and high accuracy requirement of the system. This paper presents a gait phase detection method based on feature selection and ensemble learning. Four kinds of features extraction methods of sEMG, including time, frequency, time/frequency domain and entropy, are quantitatively analyzed by statistical analysis and calculation complexity to select the best features set. Furthermore, a multiclass classifier using Light Gradient Boosting Machine (LightGBM) is first introduced in gait recognition for discriminating six different gait phases with an average accuracy (94.1%) in a reasonable calculation time (85Â ms), and the average accuracy is 5%, which is better than the traditional multiple classifiers decision fusion model. The proposed robust algorithm can effectively reduce the effect of speed on the result, which make it a perfect choice for gait phase detection.
KW - Classifier
KW - Features extraction
KW - Gait phase detection
KW - LightGBM
KW - sEMG
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U2 - 10.1007/978-981-13-7986-4_13
DO - 10.1007/978-981-13-7986-4_13
M3 - Conference contribution
AN - SCOPUS:85065716656
SN - 9789811379857
T3 - Communications in Computer and Information Science
SP - 138
EP - 149
BT - Cognitive Systems and Signal Processing - 4th International Conference, ICCSIP 2018, Revised Selected Papers
A2 - Hu, Dewen
A2 - Sun, Fuchun
A2 - Liu, Huaping
PB - Springer-Verlag
T2 - 4th International Conference on Cognitive Systems and Information Processing, ICCSIP 2018
Y2 - 29 November 2018 through 1 December 2018
ER -