Unlocking Gait Analysis Beyond the Gait Lab: High-Fidelity Replication of Knee Kinematics Using Inertial Motion Units and a Convolutional Neural Network
The findings highlight the potential of leveraging raw data from wearable inertial sensors and machine learning algorithms to reproduce gait lab-quality kinematic data outside the laboratory settings for the study of knee function following joint injury, surgery, or the progression of joint disease.
Authors
- Ivan Poupyrev (ORCID: https://orcid.org/0009-0008-7389-4726)
- Stefano A. Bini (ORCID: https://orcid.org/0000-0001-9151-7643)
- Thomas A. Peterson (ORCID: https://orcid.org/0000-0002-2562-6574)
- Richard B. Souza (ORCID: https://orcid.org/0000-0001-7556-4132)
- Brooke Schultz (ORCID: https://orcid.org/0000-0003-4407-395X)
- Nicholas Gillian
- Agnieszka Barbara Szczotka (ORCID: https://orcid.org/0000-0002-7393-0538)
- Wojciech Mormul
- Marek K Cichoń
Institutions
- Google (United States) (US)
- University of California, San Francisco (US)
- American Physical Therapy Association (US)
Publication Details
- Journal
- Arthroplasty Today
- Published
- 2025-04-15
- DOI
- https://doi.org/10.1016/j.artd.2025.101656
- Citations
- 1
- Primary Topic
- Gait Recognition and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.67