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A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
Published on: November 6, 2015
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Hand movements classification for myoelectric control system using adaptive resonance theory.
H Jahani Fariman1, Siti A Ahmad2, M Hamiruce Marhaban2
1Control System and Signal Processing Research Group, Department of Electrical & Electronic Engineering, Faculty of Engineering, University Putra Malaysia UPM, 43400, Serdang, Selangor, Malaysia. ee.hessam.jahani@gmail.com.
Australasian Physical & Engineering Sciences in Medicine
|November 20, 2015
Summary
This study introduces an efficient prosthetic hand movement classification method using surface myoelectric signals. The novel technique achieves high accuracy (89.09%) and faster processing times for prosthetic control.
Area of Science:
- Biomedical Engineering
- Rehabilitation Technology
- Signal Processing
Background:
- Surface myoelectric signals are crucial for prosthetic limb control.
- Existing methods for myoelectric signal classification can be computationally intensive.
- Accurate and efficient movement classification is essential for intuitive prosthetic hand function.
Purpose of the Study:
- To develop a simple, accurate, and computationally efficient movement classification technique for prosthetic hand applications.
- To evaluate a novel combined time-domain feature extraction method.
- To compare the proposed method with existing feature extraction techniques.
Main Methods:
- Acquisition of surface myoelectric signals from four muscles (flexor carpi ulnaris, extensor carpi radialis, biceps brachii, triceps brachii) in normal-limb subjects.
- Segmentation and feature extraction using a new combined time-domain method.
- Classification using a hybrid Adaptive Resonance Theory-based neural network.
- Performance evaluation using Fuzzy C-means clustering and scatter plots, comparing proposed features against Hudgins' multi-feature.
Main Results:
- The proposed combined time-domain feature extraction method demonstrated effectiveness.
- The hybrid Adaptive Resonance Theory classifier achieved a classification accuracy of 89.09%.
- The new technique significantly improved computation time compared to existing methods.
Conclusions:
- The developed movement classification technique is simple, accurate, and computationally efficient for prosthetic hand control.
- The hybrid classifier shows promise for real-time prosthetic applications.
- Further research can explore this method for more complex prosthetic functionalities.
