Self-Supervised Learning Improves Accuracy and Data Efficiency for IMU-Based Ground Reaction Force Estimation-Reference-Cited by-同舟云学术

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Self-Supervised Learning Improves Accuracy and Data Efficiency for IMU-Based Ground Reaction Force Estimation

Published:2024-07 Issue:7 Volume:71 Page:2095-2104
ISSN:0018-9294
Container-title:IEEE Transactions on Biomedical Engineering
language:
Short-container-title:IEEE Trans. Biomed. Eng.

Author:

Tan Tian¹^ORCID,Shull Peter B.²^ORCID,Hicks Jenifer L.³^ORCID,Uhlrich Scott D.³^ORCID,Chaudhari Akshay S.⁴^ORCID

Affiliation:

1. Department of Radiology, Stanford University, USA

2. State Key Laboratory of Mechanical System and Vibration, School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, China

3. Department of Bioengineering, Stanford University, USA

4. Department of Radiology and Biomedical Data Science, Stanford University, USA

Funder

Joe and Clara Tsai Foundation

Wu Tsai Human Performance Alliance

National Institutes of Health

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/10/10564800/10420510.pdf?arnumber=10420510

Reference55 articles.

1. Analysis of vertical ground reaction force variables during a Sit to Stand task in participants recovering from a hip fracture

2. The knee adduction moment during gait in subjects with knee osteoarthritis is more closely correlated with static alignment than radiographic disease severity, toe out angle and pain

3. Knee adduction moment, serum hyaluronan level, and disease severity in medial tibiofemoral osteoarthritis

4. Dynamic load at baseline can predict radiographic disease progression in medial compartment knee osteoarthritis

5. A systematic review of different jump-landing variables in relation to injuries;Aerts;J. Sports Med. Phys. Fitness,2013

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