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

Journal of NeuroEngineering and Rehabilitation

Research

Levi J. Hargrove¹  Minjae Kim¹ 

[1] Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, USA;Regenstein Center for Bionic Medicine, Shirley Ryan AbilityLab, Chicago, IL, USA;

关键词: Generative adversarial network;    Benchmark data;    Impedance control;    Synthetic impedance parameters;   

DOI  :  10.1186/s12984-023-01232-6

 received in 2023-01-05, accepted in 2023-08-08,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundProsthetic 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.MethodsThe conditional GAN is trained on benchmark datasets that contain the gait data of individuals without amputation; synthetic gait patterns are generated from the user input. Further, a DNN-based controller for the generation of impedance parameters is trained using the synthetic gait pattern and the corresponding synthetic stiffness and damping coefficients.ResultsThe trained GAN generated synthetic gait patterns with a coefficient of determination of 0.97 and a structural similarity index of 0.94 relative to benchmark data that were not included in the training datasets. We trained a DNN-based controller using the GAN-generated synthetic gait patterns for level-ground walking, standing-to-sitting motion, and sitting-to-standing motion. Four individuals without amputation participated in bypass testing and demonstrated the ambulation modes. The model successfully generated control parameters for the knee and ankle based on thigh angle and vertical load.ConclusionsThis study demonstrates that synthetic gait patterns can be used to train DNN models for impedance control. We believe a conditional GAN trained on benchmark datasets can provide reliable gait data for ambulation modes that are not included in its training datasets. Thus, designing gait data using a conditional GAN could facilitate the efficient and effective training of controllers for prosthetic legs.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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