Article

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.
CI - (c) The International Society for Prosthetics and Orthotics 2015.

Langue : ANGLAIS