We have located links that may give you full text access.
Journal Article
Research Support, Non-U.S. Gov't
Motion recognition for simultaneous control of multifunctional transradial prostheses.
Electromyography (EMG) pattern-recognition based control strategies for multifunctional myoelectric prosthesis systems have been studied commonly in a controlled laboratory setting. Most previous efforts concentrated on evaluating the performance of EMG pattern-recognition algorithms in identifying one signal movement at a time. Therefore, the current motion classification methods would be limited with the difficulties in identifying the combined upper-limb motion classes that are commonly required in performing activities daily. In this paper, four improved classifier training schemes were proposed and investigated to address the difficulties mentioned above. Our preliminary results showed that three of the four proposed training schemes could improve the classification performance. The average classification accuracies of the three methods were 75.10% ± 9.71%, 76.95% ± 8.02%, and 77.56% ± 6.55% for the able-bodied subjects, and 63.38% ± 7.51%, 62.55% ± 9.06%, and 62.50% ± 9.36% for the transradial amputees, respectively. These results suggested that the proposed methods could provide better classification performance in identifying the combined motions than the current methods.
Full text links
Related Resources
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app
All material on this website is protected by copyright, Copyright © 1994-2024 by WebMD LLC.
This website also contains material copyrighted by 3rd parties.
By using this service, you agree to our terms of use and privacy policy.
Your Privacy Choices
You can now claim free CME credits for this literature searchClaim now
Get seemless 1-tap access through your institution/university
For the best experience, use the Read mobile app