Title:
Multi-Context, User-Independent, Real-Time Intent Recognition for Powered Lower-Limb Prostheses
Multi-Context, User-Independent, Real-Time Intent Recognition for Powered Lower-Limb Prostheses
No Thumbnail Available
Author(s)
Bhakta, Krishan
Maldonado-Contreras, Jairo
Camargo, Jonathan
Zhou, Sixu
Compton, William
Herrin, Kinsey R.
Young, Aaron
Maldonado-Contreras, Jairo
Camargo, Jonathan
Zhou, Sixu
Compton, William
Herrin, Kinsey R.
Young, Aaron
Advisor(s)
Editor(s)
Collections
Supplementary to
Permanent Link
Abstract
Community ambulation is a critical component in maintaining a healthy lifestyle but has numerous task demands that can be challenging for individuals with limb loss. In wearable robotics, specifically powered prostheses, a need exists to provide intuitive and seamless assistance to the user. We developed a user-independent and multi-context, intent recognition system that was deployed in real-time to an open-source knee and ankle powered prosthesis (OSL). The intent recognition system predicted user intent and environment attributes using embedded sensing and control. Eleven individuals with transfemoral amputation were recruited for this study, in which 7 individuals were used for real-time validation. Here, we proposed a hierarchical control framework in which the intelligent prosthesis would first predict locomotion mode and subsequently estimate an environmental variable (i.e., walking speed or slope). Two main conclusions were found: 1) the user-independent (IND) performance across mode, speed, and slope was not statistically different from user-dependent (DEP) models in real-time, even though the offline performance of the IND system was worse 2) IND walking speed estimates showed ~0.09 m/s average error and slope estimates showed ~0.95 deg average error, which provided acceptable performance for modulating ankle and knee assistance across multi-context scenarios. Our study suggests that intelligent controllers can generalize to individuals and can perform well in real-time. In addition, we made our training dataset and the developed machine learning models publicly available to an open-source repository. This approach provides novel prosthesis users with autonomous and task-dependent functionality across real-world walking tasks.
Sponsor
Prosthetics Outcomes Research Award No. W81XWH-17-1-0031
Date Issued
2023
Extent
Resource Type
Dataset