From Pixels to Newtons: Predicting In Vivo Joint Contact Forces from Monocular Video
Quick Answer
This paper shows that A novel pipeline predicts 3D hip and knee contact forces from uncalibrated monocular video, achieving accuracy comparable to subject-specific musculoskeletal simulations with RMSE of 0.32 BW for hip and 0.23 BW for knee.
Quick Take
A novel pipeline predicts 3D hip and knee contact forces from uncalibrated monocular video, achieving accuracy comparable to subject-specific musculoskeletal simulations with RMSE of 0.32 BW for hip and 0.23 BW for knee. This method opens avenues for analyzing clinical recordings and enhancing rehabilitation tracking without invasive measures.
Key Points
- Predicts joint contact forces without markers or invasive methods.
- Achieves RMSE of 0.32 BW for hip and 0.23 BW for knee.
- Utilizes a transformer model to decode kinematic features into forces.
- Rivals existing methods in zero-shot testing on independent cohorts.
- Enables retrospective analysis of archived clinical videos.
Article Content
From source RSS / original summaryarXiv:2606. 06631v1 Announce Type: new Abstract: Joint contact forces govern implant longevity, cartilage health, and rehabilitation outcomes, shaping who develops osteoarthritis, who recovers well from joint replacement, and who benefits from biomechanical interventions. Yet they remain measurable only invasively, in a few dozen patients with instrumented implants.
I present a physics-free pipeline to predict instantaneous 3D hip and knee contact forces from an uncalibrated monocular video: no markers, force plates, electromyography, subject-specific imaging, or musculoskeletal model.
Parametric body meshes are recovered per frame, encoded as kinematic features, and decoded into forces by a transformer whose pose stream is adaptively modulated at every layer by body shape, joint, side, activity text, and self-supervised video tokens (V-JEPA 2), unifying hip and knee in a single model. Under leave-one-subject-out cross-validation across 26 patients and 25 activity categories from the in vivo OrthoLoad database, the pipeline matches the accuracy of subject-specific musculoskeletal simulations ($0.
32 \pm 0. 08$ BW RMSE for hip; $0. 23 \pm 0. 03$ BW for knee) and resolves peak force changes smaller than those reported for gait retraining and osteoarthritis progression. Applied zero-shot to an independent instrumented cohort, it rivals or outperforms prior published methods. Even without curated activity labels, video features alone preserve accuracy and enable end-to-end inference on raw footage.
Driven by the predictor, a generative motion prior produces biomechanically plausible variants with reduced peak loading, rediscovering strategies from the predictive simulation literature. This pipeline establishes uncalibrated monocular video as a viable modality for estimating joint loading, opening a path toward retrospective analysis of archived clinical recordings, primary-care screening, and at-home rehabilitation tracking.
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