Lower Limb Activity Recognition based on sEMG using Stacked Weighted Random Forest

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 32; p. 1
• Main Authors: Shen, Cheng, Pei, Zhongcai, Chen, Weihai, Wang, Jianhua, Wu, Xingming, Chen, Jianer
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.01.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Activity recognition; Adaptability; Algorithms; Biomedical signal; Classification; Electromyography; Electromyography - methods; Feature extraction; Humans; Knee; Legged locomotion; Long short-term memory; lower limb activity; Muscles; Pattern recognition; Pattern recognition systems; Pattern Recognition, Automated - methods; Radio frequency; Random Forest; Skin; Support Vector Machine; Support vector machines; Training; Usability
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2023.3346462

The existing surface electromyography-based pattern recognition system (sEMG-PRS) exhibits limited generalizability in practical applications. In this paper, we propose a stacked weighted random forest (SWRF) algorithm to enhance the long-term usability and user adaptability of sEMG-PRS. First, the weighted random forest (WRF) is proposed to address the issue of imbalanced performance in standard random forests (RF) caused by randomness in sampling and feature selection. Then, the stacking is employed to further enhance the generalizability of WRF. Specifically, RF is utilized as the base learner, while WRF serves as the meta-leaning layer algorithm. The SWRF is evaluated against classical classification algorithms in both online experiments and offline datasets. The offline experiments indicate that the SWRF achieves an average classification accuracy of 89.06%, outperforming RF, WRF, long short-term memory (LSTM), and support vector machine (SVM). The online experiments indicate that SWRF outperforms the aforementioned algorithms regarding long-term usability and user adaptability. We believe that our method has significant potential for practical application in sEMG-PRS.