Application of real-time machine learning to myoelectric prosthesis control: A case series in adaptive switching.

@article{Edwards2016ApplicationOR,
  title={Application of real-time machine learning to myoelectric prosthesis control: A case series in adaptive switching.},
  author={Ann L. Edwards and Michael Rory Dawson and Jacqueline S. Hebert and Craig Sherstan and Richard S. Sutton and K. Ming Chan and Patrick M. Pilarski},
  journal={Prosthetics and orthotics international},
  year={2016},
  volume={40 5},
  pages={573-81}
}
BACKGROUND Myoelectric prostheses currently used by amputees can be difficult to control. Machine learning, and in particular learned predictions about user intent, could help to reduce the time and cognitive load required by amputees while operating their prosthetic device. OBJECTIVES The goal of this study was to compare two switching-based methods of controlling a myoelectric arm: non-adaptive (or conventional) control and adaptive control (involving real-time prediction learning). STUDY… CONTINUE READING
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