Abstract
Objective:We evaluated a two-step method to improve control accuracy for a powered prosthetic leg using machine learning and adaptation, while reducing subject training time.Methods:First, information from three transfemoral amputees was grouped together, to create a baseline control system that was subsequently tested using data from a fourth subject (user-independent classification). Second, online adaptation was investigated, whereby the fourth subject’s data were used to improve the baseline control system in real-time. Results were compared for user-independent classification and for user-dependent classification (data collected from and tested in the same subject), with and without adaptation.Results:The combination of a user-independent classifier with real-time adaptation based on a unique individual’s data produced a classification error rate as low as 1.61% [0.15 standard error of the mean (SEM)] without requiring collection of initial training data from that individual. Training/testing using a subject’s own data (user-dependent classification), combined with adaptation, resulted in a classification error rate of 0.9% [0.12 SEM], which was not significantly different (P > 0.05) but required, on average, an additional 7.52 hours [0.92 SEM] of training sessions.Conclusion and Significance:We found that the combination of a user-independent dataset with adaptation resulted in error rates that were not significantly different from using a user-dependent dataset. Furthermore, this method eliminated the need for individual training sessions, saving many hours of data collection time.
Highlights
It has been estimated that there will be approximately 2.2 million amputees in the United States by 2020, and that number is expected to continue to rise [1]
Results are presented as the collective classification error (%) and standard error of the mean (SEM), while Table II separates results into steady-state and transitional errors, as well as the combined weighted average
This study shows that pairing a user-independent classification model with real-time adaptation results in comparable classification accuracies to user-dependent classification, but with a substantial reduction in individual training time
Summary
It has been estimated that there will be approximately 2.2 million amputees in the United States by 2020, and that number is expected to continue to rise [1]. Simon is with the Center for Bionic Medicine at the Shirley Ryan Ability Lab, Chicago, IL 60611 USA, and the Department of Physical Medicine and Rehabilitation at Northwestern University
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