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Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
Published on: July 22, 2014
Examining the adverse effects of limb position on pattern recognition based myoelectric control
E Scheme1, A Fougner, Ø Stavdahl
1Institute of Biomedical Engineering at the University of New Brunswick, Fredericton, Canada. escheme@unb.ca
Summary
Myoelectric control for prosthetics is sensitive to limb position changes after training. This finding highlights a gap between lab research and real-world prosthetic use, impacting pattern recognition accuracy.
Area of Science:
- Biomedical Engineering
- Rehabilitation Engineering
- Signal Processing
Background:
- Myoelectric signal pattern recognition is crucial for prosthetic device control.
- A significant gap exists between offline classification accuracy and clinical viability.
- Recent research emphasizes clinically relevant prosthetic outcomes.
Purpose of the Study:
- To investigate the impact of limb position variations on myoelectric pattern recognition robustness.
- To bridge the gap between laboratory findings and real-world prosthetic application.
- To enhance the reliability of prosthetic control systems.
Main Methods:
- Analyzing offline classification accuracy of pre-recorded myoelectric signals.
- Comparing constrained laboratory data collection with dynamic prosthetic use scenarios.
- Evaluating the effect of post-training limb position changes on pattern recognition performance.
Main Results:
- Variations in limb position after training significantly affect myoelectric pattern recognition robustness.
- Offline classification accuracy may not fully represent real-world prosthetic control performance.
- The dynamic nature of prosthetic use introduces challenges not captured in static testing.
Conclusions:
- Limb position variability is a critical factor impacting myoelectric control system performance.
- Future research must consider dynamic conditions for clinically viable prosthetic development.
- Improving robustness to limb position changes is essential for effective prosthetic control.
