Anam K, Swasono DI, Triono A, Muttaqin AZ, Hanggara FS. Random forest-based simultaneous and proportional
myoelectric control system for finger movements.
Comput Methods Biomech Biomed Engin 2023;
26:2057-2069. [PMID:
36649195 DOI:
10.1080/10255842.2023.2165068]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2021] [Revised: 12/07/2022] [Accepted: 12/31/2022] [Indexed: 01/18/2023]
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.
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