Generating synthetic gait patterns based on benchmark datasets for controlling prosthetic legs.

ABSTRACT:

Background

Prosthetic legs help individuals with an amputation regain locomotion. Recently, deep neural network (DNN)-based control methods, which take advantage of the end-to-end learning capability of the network, have been proposed. One prominent challenge for these learning-based approaches is obtaining data for the training, particularly for the training of a mid-level controller. In this study, we propose a method for generating synthetic gait patterns (vertical load and lower limb joint angles) using a generative adversarial network (GAN). This approach enables a mid-level controller to execute ambulation modes that are not included in the training datasets.

Methods

The conditional GAN is trained on benchmark datasets that contain the gait data of individuals without

SUBMITTER: Kim M 

PROVIDER: S-EPMC10476332 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature