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Published on: September 28, 2019
EMG pattern recognition control of multifunctional prostheses by transradial amputees
1Neural Engineering Center for Artificial Limbs, Rehabilitation Institute of Chicago and Department of Physical Medicine and Rehabilitation, Northwestern University, IL 60611, USA. gl.li@sub.siat.ac.cn
Summary
Transradial amputees can achieve basic prosthetic control using electromyogram (EMG) pattern recognition. However, complex hand movements show higher error rates compared to intact limbs.
Area of Science:
- Biomedical Engineering
- Rehabilitation Technology
- Neuroprosthetics
Background:
- Electromyogram (EMG) pattern recognition is effective for controlling prosthetic limbs in able-bodied individuals.
- Performance of EMG pattern recognition in transradial amputees for prosthetic control remains under-investigated.
Purpose of the Study:
- To evaluate the performance of EMG pattern recognition for controlling multifunctional transradial prostheses in amputees.
- To compare the control accuracy between residual and intact arms in unilateral below-elbow amputees.
Main Methods:
- Five unilateral transradial amputees participated in the study.
- EMG signals were recorded from residual and intact arms to classify ten motion classes and a 'no movement' class.
- Classification error was analyzed for basic and comprehensive motion sets.
Main Results:
- Amputated arms showed a 21% average classification error for ten motion classes, approximately 15% higher than intact arms.
- For six basic motion classes, residual arms had a 7% average classification error, comparable to intact arms (6%).
- Six optimal EMG channels were sufficient for effective control of basic movements.
Conclusions:
- Residual forearm muscles provide adequate EMG information for basic prosthetic movement control.
- EMG pattern recognition shows potential for intuitive prosthetic control in transradial amputees for essential functions.
- Limitations exist for controlling complex, fine-tuned hand and finger movements due to insufficient myoelectric information.
