@article{ref1,
title="Simulating human walking: a model-based reinforcement learning approach with musculoskeletal modeling",
journal="Frontiers in neurorobotics",
year="2023",
author="Su, Binbin and Gutierrez-Farewik, Elena M.",
volume="17",
number="",
pages="e1244417-e1244417",
abstract="INTRODUCTION: Recent advancements in reinforcement learning algorithms have accelerated the development of control models with high-dimensional inputs and outputs that can reproduce human movement. However, the produced motion tends to be less human-like if algorithms do not involve a biomechanical human model that accounts for skeletal and muscle-tendon properties and geometry. In this study, we have integrated a reinforcement learning algorithm and a musculoskeletal model including trunk, pelvis, and leg segments to develop control modes that drive the model to walk. <br><br>METHODS: We simulated human walking first without imposing target walking speed, in which the model was allowed to settle on a stable walking speed itself, which was 1.45 m/s. A range of other speeds were imposed for the simulation based on the previous self-developed walking speed. All simulations were generated by solving the Markov decision process problem with covariance matrix adaptation evolution strategy, without any reference motion data. <br><br>RESULTS: Simulated hip and knee kinematics agreed well with those in experimental observations, but ankle kinematics were less well-predicted. <br><br>DISCUSSION: We finally demonstrated that our reinforcement learning framework also has the potential to model and predict pathological gait that can result from muscle weakness.<p /> <p>Language: en</p>",
language="en",
issn="1662-5218",
doi="10.3389/fnbot.2023.1244417",
url="http://dx.doi.org/10.3389/fnbot.2023.1244417"
}