Application of real-time machine learning to myoelectric prosthesis control: A case series in adaptive switching
Prosthetics and orthotics international, 2016•journals.sagepub.com
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 design: Case series study. Methods: We …
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 design: Case series study. Methods: We …
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 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.
Clinical relevance
Adaptive control using real-time prediction learning has the potential to help decrease both the time and the cognitive load required by amputees in real-world functional situations when using myoelectric prostheses.
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