Abstract
Limb position is a factor that negatively affects myoelectric pattern recognition classification accuracy. However, prior studies evaluating impact on real-time control for upper-limb amputees have done so without a physical prosthesis on the residual limb. It remains unclear how limb position affects real-time pattern recognition control in amputees when their residual limb is supporting various weights. We used a virtual reality target achievement control test to evaluate the effects of limb position and external load on real-time pattern recognition control in fourteen intact limb subjects and six major upper limb amputee subjects. We also investigated how these effects changed based on different control system training methods. In a static training method, subjects kept their unloaded arm by their side with the elbow bent whereas in the dynamic training method, subjects moved their arm throughout a workspace while supporting a load. When static training was used, limb position significantly affected real-time control in all subjects. However, amputee subjects were still able to adequately complete tasks in all conditions, even in untrained limb positions. Moreover, increasing external loads decreased controller performance, albeit to a lesser extent in amputee subjects. The effects of limb position did not change as load increased, and vice versa. In intact limb subjects, dynamic training significantly reduced the limb position effect but did not completely remove them. In contrast, in amputee subjects, dynamic training eliminated the limb position effect in three out of four outcome measures. However, it did not reduce the effects of load for either subject population. These findings suggest that results obtained from intact limb subjects may not generalize to amputee subjects and that advanced training methods can substantially improve controller robustness to different limb positions regardless of limb loading.
Original language | English (US) |
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Article number | 9090887 |
Pages (from-to) | 1605-1613 |
Number of pages | 9 |
Journal | IEEE Transactions on Neural Systems and Rehabilitation Engineering |
Volume | 28 |
Issue number | 7 |
DOIs | |
State | Published - Jul 2020 |
Funding
Manuscript received November 15, 2019; revised April 2, 2020; accepted April 26, 2020. Date of publication May 11, 2020; date of current version July 8, 2020. This work was supported in part by the National Institutes of Health NIH under Grant R01HD094861 and in part by the Congressionally Directed Medical Research Program under Grant W81XWH-17-1-0332. (Corresponding author: Yuni Teh.) Yuni Teh is with the Regenstein Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL 60611 USA, and also with the Department of Biomedical Engineering, Northwestern University, Chicago, IL 60611 USA (e-mail: [email protected]).
Keywords
- Prosthetics
- limb position
- myoelectric control
- pattern recognition (PR)
ASJC Scopus subject areas
- Rehabilitation
- General Neuroscience
- Internal Medicine
- Biomedical Engineering