Rehabilitation of Musculoskeletal Models Using Deep Reinforcement Learning
Visualitza/Obre
Estadístiques de LA Referencia / Recolecta
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/361640
Tipus de documentText en actes de congrés
Data publicació2021
Condicions d'accésAccés obert
Llevat que s'hi indiqui el contrari, els
continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
:
Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
Abstract
Neural rehabilitation is a long and complex process that patients undergo after suffering a nervous system injury, such as stroke. These kinds of injuries usually result in brain cells death and partial loss of mobility and coordination. During rehabilitation, the patient performs a series of movements and physical exercises that promote neural plasticity, the brain’s mechanism to regenerate and make new pathways that substitute the damaged connections. Unfortunately, full recovery is almost impossible. The rehabilitation process is tailored to the patient based on the physician’s expertise, and it evolves with the patient’s needs and recovery. However, few computational models for rehabilitation have been developed. For instance, Lee et al. [1] trained a musculoskeletal model of a healthy subject using deep reinforcement learning, and then a prosthetic leg was added to simulate an injury. Results showed how the artificial neural network that controlled muscle contraction was able to adapt and learn to move with the prosthetic leg. Here we show how deep reinforcement learning can be used to control a musculoskeletal model. The algorithm is able to learn new and stable motions by maximizing the so-called reward function. The nervous system is modelled with an artificial neural network, and the deep deterministic policy gradient (DDPG) algorithm is used to train the model in a simulated environment.
CitacióMadorell, Q.; Peiret, A.; Font-Llagunes, J.M. Rehabilitation of Musculoskeletal Models Using Deep Reinforcement Learning. A: ECCOMAS Thematic Conference on Multibody Dynamics. "Book of Abstracts of the 10th ECCOMAS Thematic Conference on Multibody Dynamics". 2021, p. 56-57. ISBN 978-963-421-869-2.
ISBN978-963-421-869-2
Altres identificadorshttps://eccomasmultibody2021.mm.bme.hu/Files/EccomasMultybody2021_Abstracts.pdf
Fitxers | Descripció | Mida | Format | Visualitza |
---|---|---|---|---|
2021_eccomas_RL.pdf | 396,7Kb | Visualitza/Obre |