Random forest-based simultaneous and proportional myoelectric control system for finger movements

Khairul Anam, Dwiretno Istiyadi Swasono, Agus Triono, Aris Z. Muttaqin and Faruq S. Hanggara

Computer Methods in Biomechanics and Biomedical Engineering, 2023, vol. 26, issue 16, 2057-2069

Abstract: A classification scheme for myoelectric control systems (MCS) cannot mimic complex hand movements. This paper presents simultaneous and proportional MCS by estimating the angles of fourteen finger joints using time-domain feature extraction and random forest. The experimental results show that the best feature was the root mean square (RMS). Furthermore, the random forest attained an average coefficient of determination (R2) of 0.85 compared to other regressors which perform below 0.75. The ANOVA tests indicated that the performance of the proposed system was significantly different. Therefore, the proposed system will be the best option for real-time MCS applications in the future.

Date: 2023
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DOI: 10.1080/10255842.2023.2165068

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