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Resolving the effect of wrist position on myoelectric pattern recognition control
Adenike A Adewuyi1,2,3, Levi J Hargrove4,5,6, Todd A Kuiken4,5,7,6
1Department of Biomedical Engineering, Northwestern University, Chicago, IL, USA. a-adewuyi@fsm.northwestern.edu.
Journal of Neuroengineering and Rehabilitation
|May 6, 2017
Summary
This study demonstrates that combining EMG signals with wrist position data significantly improves prosthetic hand control for partial-hand amputees. A novel wrist position-independent controller enhances performance by up to 74%.
Area of Science:
- Biomedical Engineering
- Rehabilitation Technology
- Prosthetics
Background:
- Pattern recognition for myoelectric prostheses is established for high-level amputations.
- Limited research exists on its efficacy for partial-hand amputees retaining wrist function.
Purpose of the Study:
- To evaluate strategies for partial-hand amputees to control prosthetic hands while preserving wrist function.
- To assess classification schemes using EMG and wrist position data.
Main Methods:
- Recorded EMG data from extrinsic and intrinsic hand muscles in non-amputees and partial-hand amputees.
- Evaluated four classification schemes with EMG data alone and combined with wrist position data.
- Developed a wrist position-independent controller using EMG and wrist positional data.
Main Results:
- A multi-layer perceptron artificial neural network outperformed other classifiers.
- Integrating wrist position data with EMG significantly improved classification performance.
- A proposed wrist position-independent controller improved performance by 45-74% for amputees and non-amputees.
Conclusions:
- Sensor fusion (EMG and wrist position), non-linear neural networks, and multi-muscle EMG data improve classification.
- Simulating data across wrist positions effectively mitigates the wrist position effect.
- Developed strategies enhance prosthetic hand control for partial-hand amputees.
