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.
Highlights
I N THE United States, there are approximately 41,000 people living with a major upper limb amputation, 40% of whom are transradial amputees [1], [2]
We evaluated the effects of limb position, external load, and training method on pattern recognition (PR)-based myoelectric control in fourteen subjects with intact limbs and six subjects with major upper limb amputations (Table I)
We evaluated the effects of limb position, load, and training method on real-time pattern recognition control in intact limb and amputee subjects
Summary
I N THE United States, there are approximately 41,000 people living with a major upper limb amputation, 40% of whom are transradial amputees [1], [2]. These individuals have difficulty performing essential activities of daily living, such as eating, grooming, and dressing [3]–[6]. Myoelectric prostheses have the potential to restore the function required to regain basic independence. Despite decades of advancements, challenges persist in controlling myoelectric prostheses. This remains an active area of research and several methods have been proposed to improve control
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IEEE Transactions on Neural Systems and Rehabilitation Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
