Gait Speed and Task Specificity in Predicting Lower-Limb Kinematics: A Deep Learning Approach Using Inertial Sensors
Our findings underscore the potential of IMU-based deep learning models for joint kinematic predictions, offering a practical solution for remote and continuous biomechanical assessments in health care and sports science.
Authors
- Philippe C. Dixon (ORCID: https://orcid.org/0000-0003-3581-7259)
- Vaibhav R Shah (ORCID: https://orcid.org/0000-0002-2275-8678)
Institutions
- Centre Hospitalier Universitaire Sainte-Justine (CA)
- McGill University (CA)
- Université de Montréal (CA)
Publication Details
- Journal
- Mayo Clinic Proceedings Digital Health
- Published
- 2024-11-27
- DOI
- https://doi.org/10.1016/j.mcpdig.2024.11.004
- Citations
- 3
- Primary Topic
- Muscle activation and electromyography studies
- Type
- article
- Field-Weighted Citation Impact
- 0.55
Funders
- McGill University
- Canada First Research Excellence Fund
- Fonds de recherche du Québec
- Natural Sciences and Engineering Research Council of Canada
- Fonds de Recherche du Québec - Santé