A computer vision-based modeling pipeline to facilitate efficient video-to-strain analysis of human tibia during dynamic gait.

Background and objective

Tibial stress and strain are crucial indicators of bone health and injury risk, yet their estimation with conventional marker-based motion capture systems is time-consuming and constrained to multi-camera laboratory setups, limiting broader applicability.

Methods

We propose a novel video-to-strain analysis (V2S) method, enabling subject-specific musculoskeletal modeling and tibial stress/strain computation from single-view smartphone recordings of treadmill walking. Markerless motion was reconstructed using SMPL-based fitting with HuMoR, a deep generative human motion prior, together with PlaneRCNN ground-plane estimation and contact and bilateral symmetry constraints to yield more stable 3D pose and gait reconstruction from single-view camera. Feasibility was evaluated by comparing kinematics, muscle forces, and finite element-computed tibial stress/strain against a marker-based reference workflow.

Results

Compared to the marker-based method, the proposed markerless method yielded joint kinematics with R = 0.86 and muscle forces with R = 0.81. Relative to the baseline HuMoR model, our approach achieved lower kinematic errors (hip, knee, and ankle RMSE of 3.6°, 4.8°, and 4.0°, compared with 9.1°, 11.6°, and 12.3° for HuMoR). The model-computed results with peak von Mises stress varying by 3.8% and the maximum principal strain differing by 11.9%, with the largest discrepancies occurring in stance-phase.

Conclusions

Single-view markerless approach offers a practical alternative for efficient motion and bone strain analysis, where marker-based motion capture is not feasible.

Keywords

Computational model, Gait analysis, Ground reaction force estimation, Markerless motion capture, Tibial strain

Conflict of interest statement

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.