Lower Limb Activity Recognition using sEMG Signals via Weighted Random Forest

Cheng Shen; Zhongcai Pei; Weihai Chen; Jianhua Wang; Jianbin Zhang; Jianer Chen; Mingxing Lyu

doi:10.1109/ICIEA54703.2022.10005913

Lower Limb Activity Recognition using sEMG Signals via Weighted Random Forest

Cheng Shen
, Zhongcai Pei^*
, Weihai Chen
, Jianhua Wang
, Jianbin Zhang
, Jianer Chen
, Mingxing Lyu

^*Corresponding author for this work

School of Automation Science and Electrical Engineering

Beihang University
Zhejiang Chinese Medical University
Nanjing Research Institute of Electronics Technology

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

9 Scopus citations

Abstract

Identifying the motion intention of the wearer and integrating it into the lower limb rehabilitation device is a rehabilitation interaction method that can effectively improve the rehabilitation effect. Existing surface electromyography (sEMG) based intention identification methods have the shortcomings of limited generalization ability in practical applications. In this paper, we proposed a weighted random forest (WRF) algorithm for the intention prediction. The performance of the ensemble model is improved by weighted ensemble of each base prediction decision tree. Detailed comparative studies between proposed method and conventional methods have been carried out through the lower limb activity identification data at different time periods. Compared with Linear Discriminant Analysis (LDA), Gradient Boosting (GB), AdaBoost, K-Nearest Neighbor (KNN), Random Forest (RF), the generalization ability of WRF is significantly superior considering the best accuracy and the best precision achieved by WRF. Compared to standard RF, the WRF improved classification accuracy performance by 1.19% and the classification precision accuracy performance by 1.1% on average. Conclusively, the proposed method can provide satisfactory generalization performance.

Original language	English
Title of host publication	ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications
Editors	Wenxiang Xie, Shibin Gao, Xiaoqiong He, Xing Zhu, Jingjing Huang, Weirong Chen, Lei Ma, Haiyan Shu, Wenping Cao, Lijun Jiang, Zeliang Shu
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1151-1156
Number of pages	6
ISBN (Electronic)	9781665409841
DOIs	https://doi.org/10.1109/ICIEA54703.2022.10005913
State	Published - 2022
Event	17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022 - Chengdu, China Duration: 16 Dec 2022 → 19 Dec 2022

Publication series

Name	ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications

Conference

Conference	17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022
Country/Territory	China
City	Chengdu
Period	16/12/22 → 19/12/22

Keywords

Biomedical signal
classification
human activity recognition
random forests
surface electromyography

Access to Document

10.1109/ICIEA54703.2022.10005913

Cite this

Shen, C., Pei, Z., Chen, W., Wang, J., Zhang, J., Chen, J., & Lyu, M. (2022). Lower Limb Activity Recognition using sEMG Signals via Weighted Random Forest. In W. Xie, S. Gao, X. He, X. Zhu, J. Huang, W. Chen, L. Ma, H. Shu, W. Cao, L. Jiang, & Z. Shu (Eds.), ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications (pp. 1151-1156). (ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICIEA54703.2022.10005913

Shen, Cheng ; Pei, Zhongcai ; Chen, Weihai et al. / Lower Limb Activity Recognition using sEMG Signals via Weighted Random Forest. ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications. editor / Wenxiang Xie ; Shibin Gao ; Xiaoqiong He ; Xing Zhu ; Jingjing Huang ; Weirong Chen ; Lei Ma ; Haiyan Shu ; Wenping Cao ; Lijun Jiang ; Zeliang Shu. Institute of Electrical and Electronics Engineers Inc., 2022. pp. 1151-1156 (ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications).

@inproceedings{3dc0c6c7fff249c2b51f9bfb5cd9b69c,

title = "Lower Limb Activity Recognition using sEMG Signals via Weighted Random Forest",

abstract = "Identifying the motion intention of the wearer and integrating it into the lower limb rehabilitation device is a rehabilitation interaction method that can effectively improve the rehabilitation effect. Existing surface electromyography (sEMG) based intention identification methods have the shortcomings of limited generalization ability in practical applications. In this paper, we proposed a weighted random forest (WRF) algorithm for the intention prediction. The performance of the ensemble model is improved by weighted ensemble of each base prediction decision tree. Detailed comparative studies between proposed method and conventional methods have been carried out through the lower limb activity identification data at different time periods. Compared with Linear Discriminant Analysis (LDA), Gradient Boosting (GB), AdaBoost, K-Nearest Neighbor (KNN), Random Forest (RF), the generalization ability of WRF is significantly superior considering the best accuracy and the best precision achieved by WRF. Compared to standard RF, the WRF improved classification accuracy performance by 1.19\% and the classification precision accuracy performance by 1.1\% on average. Conclusively, the proposed method can provide satisfactory generalization performance.",

keywords = "Biomedical signal, classification, human activity recognition, random forests, surface electromyography",

author = "Cheng Shen and Zhongcai Pei and Weihai Chen and Jianhua Wang and Jianbin Zhang and Jianer Chen and Mingxing Lyu",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022 ; Conference date: 16-12-2022 Through 19-12-2022",

year = "2022",

doi = "10.1109/ICIEA54703.2022.10005913",

language = "英语",

series = "ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1151--1156",

editor = "Wenxiang Xie and Shibin Gao and Xiaoqiong He and Xing Zhu and Jingjing Huang and Weirong Chen and Lei Ma and Haiyan Shu and Wenping Cao and Lijun Jiang and Zeliang Shu",

booktitle = "ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications",

address = "美国",

}

Shen, C, Pei, Z, Chen, W, Wang, J, Zhang, J, Chen, J & Lyu, M 2022, Lower Limb Activity Recognition using sEMG Signals via Weighted Random Forest. in W Xie, S Gao, X He, X Zhu, J Huang, W Chen, L Ma, H Shu, W Cao, L Jiang & Z Shu (eds), ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications. ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications, Institute of Electrical and Electronics Engineers Inc., pp. 1151-1156, 17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022, Chengdu, China, 16/12/22. https://doi.org/10.1109/ICIEA54703.2022.10005913

Lower Limb Activity Recognition using sEMG Signals via Weighted Random Forest. / Shen, Cheng; Pei, Zhongcai; Chen, Weihai et al.
ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications. ed. / Wenxiang Xie; Shibin Gao; Xiaoqiong He; Xing Zhu; Jingjing Huang; Weirong Chen; Lei Ma; Haiyan Shu; Wenping Cao; Lijun Jiang; Zeliang Shu. Institute of Electrical and Electronics Engineers Inc., 2022. p. 1151-1156 (ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Lower Limb Activity Recognition using sEMG Signals via Weighted Random Forest

AU - Shen, Cheng

AU - Pei, Zhongcai

AU - Chen, Weihai

AU - Wang, Jianhua

AU - Zhang, Jianbin

AU - Chen, Jianer

AU - Lyu, Mingxing

PY - 2022

Y1 - 2022

N2 - Identifying the motion intention of the wearer and integrating it into the lower limb rehabilitation device is a rehabilitation interaction method that can effectively improve the rehabilitation effect. Existing surface electromyography (sEMG) based intention identification methods have the shortcomings of limited generalization ability in practical applications. In this paper, we proposed a weighted random forest (WRF) algorithm for the intention prediction. The performance of the ensemble model is improved by weighted ensemble of each base prediction decision tree. Detailed comparative studies between proposed method and conventional methods have been carried out through the lower limb activity identification data at different time periods. Compared with Linear Discriminant Analysis (LDA), Gradient Boosting (GB), AdaBoost, K-Nearest Neighbor (KNN), Random Forest (RF), the generalization ability of WRF is significantly superior considering the best accuracy and the best precision achieved by WRF. Compared to standard RF, the WRF improved classification accuracy performance by 1.19% and the classification precision accuracy performance by 1.1% on average. Conclusively, the proposed method can provide satisfactory generalization performance.

AB - Identifying the motion intention of the wearer and integrating it into the lower limb rehabilitation device is a rehabilitation interaction method that can effectively improve the rehabilitation effect. Existing surface electromyography (sEMG) based intention identification methods have the shortcomings of limited generalization ability in practical applications. In this paper, we proposed a weighted random forest (WRF) algorithm for the intention prediction. The performance of the ensemble model is improved by weighted ensemble of each base prediction decision tree. Detailed comparative studies between proposed method and conventional methods have been carried out through the lower limb activity identification data at different time periods. Compared with Linear Discriminant Analysis (LDA), Gradient Boosting (GB), AdaBoost, K-Nearest Neighbor (KNN), Random Forest (RF), the generalization ability of WRF is significantly superior considering the best accuracy and the best precision achieved by WRF. Compared to standard RF, the WRF improved classification accuracy performance by 1.19% and the classification precision accuracy performance by 1.1% on average. Conclusively, the proposed method can provide satisfactory generalization performance.

KW - Biomedical signal

KW - classification

KW - human activity recognition

KW - random forests

KW - surface electromyography

UR - https://www.scopus.com/pages/publications/85146898329

U2 - 10.1109/ICIEA54703.2022.10005913

DO - 10.1109/ICIEA54703.2022.10005913

M3 - 会议稿件

AN - SCOPUS:85146898329

T3 - ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications

SP - 1151

EP - 1156

BT - ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications

A2 - Xie, Wenxiang

A2 - Gao, Shibin

A2 - He, Xiaoqiong

A2 - Zhu, Xing

A2 - Huang, Jingjing

A2 - Chen, Weirong

A2 - Ma, Lei

A2 - Shu, Haiyan

A2 - Cao, Wenping

A2 - Jiang, Lijun

A2 - Shu, Zeliang

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 17th IEEE Conference on Industrial Electronics and Applications, ICIEA 2022

Y2 - 16 December 2022 through 19 December 2022

ER -

Shen C, Pei Z, Chen W, Wang J, Zhang J, Chen J et al. Lower Limb Activity Recognition using sEMG Signals via Weighted Random Forest. In Xie W, Gao S, He X, Zhu X, Huang J, Chen W, Ma L, Shu H, Cao W, Jiang L, Shu Z, editors, ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications. Institute of Electrical and Electronics Engineers Inc. 2022. p. 1151-1156. (ICIEA 2022 - Proceedings of the 17th IEEE Conference on Industrial Electronics and Applications). doi: 10.1109/ICIEA54703.2022.10005913

Lower Limb Activity Recognition using sEMG Signals via Weighted Random Forest

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