Machine Learning Applications in Spine Biomechanics
Farshid Ghezelbash Amir Hossein Eskandari Xavier Robert-Lachaine Frank Cao Mehran Pesteie Zhuohua Qiao Aboulfazl Shirazi-Adl Christian Larivière
Spine biomechanics is at a transformation with the advent and integration of
machine learning and computer vision technologies. These novel techniques
facilitate the estimation of 3D body shapes, anthropometrics, and kinematics
from as simple as a single-camera image, making them more accessible and
practical for a diverse range of applications. This study introduces a
framework that merges these methodologies with traditional musculoskeletal
modeling, enabling comprehensive analysis of spinal biomechanics during complex
activities from a single camera. Additionally, we aim to evaluate their
performance and limitations in spine biomechanics applications. The real-world
applications explored in this study include assessment in workplace lifting,
evaluation of whiplash injuries in car accidents, and biomechanical analysis in
professional sports. Our results demonstrate potential and limitations of
various algorithms in estimating body shape, kinematics, and conducting
in-field biomechanical analyses. In industrial settings, the potential to
utilize these new technologies for biomechanical risk assessments offers a
pathway for preventive measures against back injuries. In sports activities,
the proposed framework provides new opportunities for performance optimization,
injury prevention, and rehabilitation. The application in forensic domain
further underscores the wide-reaching implications of this technology. While
certain limitations were identified, particularly in accuracy of predictions,
complex interactions, and external load estimation, this study demonstrates
their potential for advancement in spine biomechanics, heralding an optimistic
future in both research and practical applications.