Seamless and intuitive control of a powered prosthetic leg using deep neural network for transfemoral amputees.

ABSTRACT: Powered prosthetic legs are becoming a promising option for amputee patients. However, developing safe, robust, and intuitive control strategies for powered legs remains one of the greatest challenges. Although a variety of control strategies have been proposed, creating and fine-tuning the system parameters is time-intensive and complicated when more activities need to be restored. In this study, we developed a deep neural network (DNN) model that facilitates seamless and intuitive gait generation and transitions across five ambulation modes: level-ground walking, ascending/descending ramps, and ascending/descending stairs. The combination of latent and time sequence features generated the desired impedance parameters within the ambulation modes and allowed seamless transitions between am

SUBMITTER: Kim M 

PROVIDER: S-EPMC10085575 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature