MENU ▼
Read by QxMD icon Read
search
OPEN IN READ APP
COMPARATIVE STUDY
JOURNAL ARTICLE

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

Ann L Edwards, Michael R Dawson, Jacqueline S Hebert, Craig Sherstan, Richard S Sutton, K Ming Chan, Patrick M Pilarski
Prosthetics and Orthotics International 2016, 40 (5): 573-81
26423106

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.

Comments

You need to log in or sign up for an account to be able to comment.

No comments yet, be the first to post one!

Related Papers

Available on the App Store

Available on the Play Store
Remove bar
Read by QxMD icon Read
26423106
×

Search Tips

Use Boolean operators: AND/OR

diabetic AND foot
diabetes OR diabetic

Exclude a word using the 'minus' sign

Virchow -triad

Use Parentheses

water AND (cup OR glass)

Add an asterisk (*) at end of a word to include word stems

Neuro* will search for Neurology, Neuroscientist, Neurological, and so on

Use quotes to search for an exact phrase

"primary prevention of cancer"
(heart or cardiac or cardio*) AND arrest -"American Heart Association"