Learning Walking on a Musculoskeletal Human System with a Prosthesis Protezli Kas-Iskelet Insan Sisteminde Yürüme Öǧrenmesi

Hakki Durmus I., Yalçın H.

30th Signal Processing and Communications Applications Conference, SIU 2022, Safranbolu, Turkey, 15 - 18 May 2022 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/siu55565.2022.9864755
  • City: Safranbolu
  • Country: Turkey
  • Keywords: deep reinforcement learning, machine learning, Musculoskeletal modeling, prosthesis
  • Istanbul Technical University Affiliated: Yes

Abstract

© 2022 IEEE.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.