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Application of Real-time Machine Learning to Myoelectric Prosthesis Control: A Case Series in Adaptive Switching

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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: nonadaptive (or conventional) control and adaptive control (involving real-time prediction learning). Study design: Case series study. Methods: We compared non-adaptive and adaptive control in two different experiments. In the first, one amputee and one non-amputee subject controlled a robotic arm to perform a simple task; in the second, three able-bodied subjects controlled a robotic arm to perform a more complex task. For both tasks, we calculated the mean time and total number of switches between robotic arm functions over three trials. Results: Adaptive control significantly decreased the number of switches and total switching time for both tasks compared with the conventional control method. Conclusion: Real-time prediction learning was successfully used to improve the control interface of a myoelectric robotic arm during uninterrupted use by an amputee subject and able-bodied subjects.

Citation

A. Edwards, M. Dawson, J. Hebert, C. Sherstan, R. Sutton, K. Chan, P. Pilarski. "Application of Real-time Machine Learning to Myoelectric Prosthesis Control: A Case Series in Adaptive Switching". Prosthetics and Orthotics International, 40(5), pp 573–581, October 2016.

Keywords: Upper limb prosthetics, prosthetics, prosthetic design, rehabilitation of amputees, rehabilitation
Category: In Journal
Web Links: doi
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BibTeX

@article{Edwards+al:16,
  author = {Ann L. Edwards and Michael R. Dawson and Jacqueline S. Hebert and
    Craig Sherstan and Richard S. Sutton and K. Ming Chan and Patrick M.
    Pilarski},
  title = {Application of Real-time Machine Learning to Myoelectric Prosthesis
    Control: A Case Series in Adaptive Switching},
  Volume = "40",
  Number = "5",
  Pages = {573–581},
  journal = {Prosthetics and Orthotics International},
  year = 2016,
}

Last Updated: November 10, 2020
Submitted by Sabina P

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