Learning to Walk on a Human Musculoskeletal Model Wearing a Knee Orthosis via Deep Reinforcement Learning

Omer Kayan, Hulya Yalcin

Araştırma sonucu: ???type-name???Konferans katkısıbilirkişi

Özet

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.

Orijinal dilİngilizce
Ana bilgisayar yayını başlığıHORA 2023 - 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9798350337525
DOI'lar
Yayın durumuYayınlandı - 2023
Etkinlik5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023 - Istanbul, Turkey
Süre: 8 Haz 202310 Haz 2023

Yayın serisi

AdıHORA 2023 - 2023 5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, Proceedings

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???event.eventtypes.event.conference???5th International Congress on Human-Computer Interaction, Optimization and Robotic Applications, HORA 2023
Ülke/BölgeTurkey
ŞehirIstanbul
Periyot8/06/2310/06/23

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Publisher Copyright:
© 2023 IEEE.

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