Learning to Walk With Deep Reinforcement Learning: Forward Dynamic Simulation of a Physics-Based Musculoskeletal Model of an Osseointegrated Transfemoral Amputee-Reference-Cited by-同舟云学术

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Learning to Walk With Deep Reinforcement Learning: Forward Dynamic Simulation of a Physics-Based Musculoskeletal Model of an Osseointegrated Transfemoral Amputee

Published:2024 Issue: Volume:32 Page:431-441
ISSN:1534-4320
Container-title:IEEE Transactions on Neural Systems and Rehabilitation Engineering
language:
Short-container-title:IEEE Trans. Neural Syst. Rehabil. Eng.

Author:

Ogum Brown N.¹,Schomaker Lambert R. B.¹^ORCID,Carloni Raffaella¹^ORCID

Affiliation:

1. Bernoulli Institute for Mathematics, Computer Science and Artificial Intelligence, Faculty of Science and Engineering, University of Groningen, Groningen, The Netherlands

Funder

European Commission’s Horizon 2020 Program as part of the Project MyLeg

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7333/10398877/10387751.pdf?arnumber=10387751

Reference28 articles.

1. Load response of an osseointegrated implant used in the treatment of unilateral transfemoral amputation: An early implant loosening case study

2. Musculoskeletal model of osseointegrated transfemoral amputees in OpenSim

3. Gait compensatory mechanisms in unilateral transfemoral amputees

4. Individual muscle contributions to hip joint-contact forces during walking in unilateral transfemoral amputees with osseointegrated prostheses

5. Dynamic input to determine hip joint moments, power and work on the prosthetic limb of transfemoral amputees

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