Real-Time Adaptation of an Artificial Neural Network for Transfemoral Amputees Using a Powered Prosthesis

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.