Abstract
Real-time pattern recognition control is frequently affected by misclassifications. This study investigated the use of a decision-based velocity ramp that attenuated movement speed after a change in classifier decision. The goal was to improve prosthesis positioning by minimizing the effect of unintended movements. Nonamputee and amputee subjects controlled a prosthesis in real time using pattern recognition. While performing a target achievement test in a virtual environment, subjects had a significantly higher completion rate ( p< 0.05) and a more direct path (p < 0.05) to the target with the velocity ramp than without it. Using a physical prosthesis, subjects stacked a greater average number of 1-in cubes (p< 0.05) in 3 min with the velocity ramp than without it (76 more blocks for nonamputees; 89 more blocks for amputees). Real-time control using the velocity ramp also showed significant performance improvements above using majority vote. Eighty-three percent of subjects preferred to control the prosthesis using the velocity ramp. These results suggest that using a decision-based velocity ramp with pattern recognition may improve user performance. Since the velocity ramp is a postprocessing step, it has the potential to be used with a variety of classifiers for many applications.
Original language | English (US) |
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Article number | 5768069 |
Pages (from-to) | 2360-2368 |
Number of pages | 9 |
Journal | IEEE Transactions on Biomedical Engineering |
Volume | 58 |
Issue number | 8 |
DOIs | |
State | Published - Aug 2011 |
Keywords
- Myoelectric control
- pattern recognition
- prosthesis
- surface electromyography (EMG)
- upper limb
ASJC Scopus subject areas
- Biomedical Engineering