Abstract
Knee orthoses aim to treat problems in the knees by customizing them in order to support the joint externally, protect the joint, provide bio-mechanical balance, eliminate dysfunctions, reduce pain, and strengthen weakened muscles. Since each case is different from each other, individual treatment is required. For this reason, measuring the performance of orthoses in a simulated environment before they are applied to the patients increases efficiency during the treatment. Musculoskeletal model simulations allow estimating how the orthosis will affect the patient's motions. In this paper, the deep reinforcement learning (DRL) method, which imitates the reference walking motion, is used in simulations for the model to learn to walk. The walking performance and muscle activation of four different musculoskeletal models that are healthy, injured in the knee but not wearing an orthosis, wearing passive orthosis, and wearing active orthosis are compared.
Original language | English |
---|---|
Title of host publication | HORA 2023 - 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9798350337525 |
DOIs | |
Publication status | Published - 2023 |
Event | 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023 - Istanbul, Turkey Duration: 8 Jun 2023 → 10 Jun 2023 |
Publication series
Name | HORA 2023 - 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings |
---|
Conference
Conference | 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023 |
---|---|
Country/Territory | Turkey |
City | Istanbul |
Period | 8/06/23 → 10/06/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Keywords
- deep reinforcement learning
- muscle activation
- Musculoskeletal modeling
- neural networks
- orthosis