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

• Published in: IEEE transactions on biomedical engineering Vol. 69; no. 3; pp. 1202 - 1211
• Main Authors: Woodward, Richard, Simon, Ann, Seyforth, Emily, Hargrove, Levi
• Format: Journal Article
• Language: English
• Published: United States IEEE 01.03.2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation; Adaptive algorithms; Amputees; Artificial Limbs; Artificial neural networks; Classification; Control systems; Data collection; Datasets; Feature extraction; Humans; Learning algorithms; Legged locomotion; Machine learning; Mechanical sensors; Neural networks; Neural Networks, Computer; Prostheses; Prosthesis Implantation; Prosthetics; Real time; Real-time systems; Stairs; Standard error; Training; Trans-femoral amputees
• ISSN: 0018-9294; 1558-2531; 1558-2531
• DOI: 10.1109/TBME.2021.3120616

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 <inline-formula><tex-math notation="LaTeX">></tex-math></inline-formula> 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.