Abstract:
Powered prostheses have the potential to restore abilities lost to amputation; however, many users report dissatisfaction with the control of their devices. The high vari...Show MoreMetadata
Abstract:
Powered prostheses have the potential to restore abilities lost to amputation; however, many users report dissatisfaction with the control of their devices. The high variability of the EMG signals used to control powered devices likely burdens amputees with high movement uncertainty. In able-bodied subjects uncertainty affects adaptation, control, and feedback processing, which are often modeled using Bayesian statistics. Understanding the role of uncertainty for amputees might thus be important for the design and control of prosthetic devices. Here we quantified the role of uncertainty using a visual trial-by-trial adaptation approach. We compared adaptation behavior with two control interfaces meant to mimic able-bodied and prosthesis control: torque control and EMG control. In both control interfaces, adaptation rate decreased with high feedback uncertainty and increased with high mean error. However, we did observe different patterns of learning as the experiment progressed. For torque control, subjects improved and consequently adapted slower as the experiment progressed, while no such improvements were made for EMG control. Thus, EMG control resulted in overall adaptation behavior that supports Bayesian models, but with altered learning patterns and higher errors. These findings encourage further studies of adaptation with powered prostheses. A better understanding of the factors that alter learning patterns and errors will help design prosthesis control systems that optimize learning and performance for the prosthesis user.
Published in: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society
Date of Conference: 26-30 August 2014
Date Added to IEEE Xplore: 06 November 2014
Electronic ISBN:978-1-4244-7929-0
ISSN Information:
PubMed ID: 25570748