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A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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Random forest-based simultaneous and proportional myoelectric control system for finger movements
Khairul Anam1,2,3, Dwiretno Istiyadi Swasono4, Agus Triono5
1Department of Electrical Engineering, University of Jember, Jember, Indonesia.
Computer Methods in Biomechanics and Biomedical Engineering
|January 17, 2023
Summary
This study introduces a new myoelectric control system (MCS) for complex hand movements. The system accurately estimates finger joint angles, offering a promising solution for advanced prosthetic control.
Area of Science:
- Biomedical Engineering
- Rehabilitation Engineering
- Human-Computer Interaction
Background:
- Current myoelectric control systems (MCS) struggle to replicate intricate hand motions.
- Advanced prosthetic control requires systems capable of mimicking natural hand dexterity.
Purpose of the Study:
- To develop a simultaneous and proportional myoelectric control system (MCS) for complex hand movements.
- To improve the accuracy and functionality of prosthetic hand control.
Main Methods:
- Utilized time-domain feature extraction and a random forest algorithm to estimate fourteen finger joint angles.
- Evaluated various features, identifying root mean square (RMS) as the most effective.
Main Results:
- The random forest regressor achieved a high average coefficient of determination (R2) of 0.85.
- Outperformed other regressors, which had R2 values below 0.75.
- ANOVA tests confirmed statistically significant performance differences, validating the proposed system's efficacy.
Conclusions:
- The proposed simultaneous and proportional MCS demonstrates superior performance in estimating finger joint angles.
- This system represents a significant advancement for real-time myoelectric control applications.
- Offers a more natural and intuitive control for prosthetic devices.
