Protezli Kas-Iskelet Insan Sisteminde Yürüme Öǧrenmesi

Ibrahim Hakki Durmus, Hulya Yalcin

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

Özet

Incompetent design of prosthesis for amputees inflict pain in muscles and bones contingent to the prosthesis. Simulation models mimicking human movement promise a prosthesis with improved movement capability for amputees. Musculoskeletal models enable better anticipation of prosthesis contributions to the human musculoskeletal system during walking movement. In this paper, we apply a simulation of musculoskeletal model on an amputated human model with a prosthesis using Gaussian Process Regression Machine Learning Predictor and deep reinforcement learning. The performance of two versions of a prosthesis, one being a simpler version (passive prosthesis) and one being relatively better version (active prosthesis) are evaluated and compared to that of a healthy human model.

Tercüme edilen katkı başlığıLearning Walking on a Musculoskeletal Human System with a Prosthesis
Orijinal dilTürkçe
Ana bilgisayar yayını başlığı2022 30th Signal Processing and Communications Applications Conference, SIU 2022
YayınlayanInstitute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)9781665450928
DOI'lar
Yayın durumuYayınlandı - 2022
Etkinlik30th Signal Processing and Communications Applications Conference, SIU 2022 - Safranbolu, Turkey
Süre: 15 May 202218 May 2022

Yayın serisi

Adı2022 30th Signal Processing and Communications Applications Conference, SIU 2022

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???event.eventtypes.event.conference???30th Signal Processing and Communications Applications Conference, SIU 2022
Ülke/BölgeTurkey
ŞehirSafranbolu
Periyot15/05/2218/05/22

Bibliyografik not

Publisher Copyright:
© 2022 IEEE.

Keywords

  • deep reinforcement learning
  • machine learning
  • Musculoskeletal modeling
  • prosthesis

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