Gait Foundation Model Predicts Multi-System Biomarkers
Researchers led by Adam Gabet et al. (submitted 26 March 2026) develop a gait foundation model for 3D skeletal motion trained on 3,414 deeply phenotyped adults recorded via a depth camera across five motor tasks. Learned embeddings outperformed engineered features, predicting age (r = 0.69), BMI (r = 0.90), and visceral adipose tissue area (r = 0.82), and significantly predicted 1,980 of 3,210 phenotypic targets. Findings motivate translation to consumer-grade video and integration of gait as a scalable, passive vital sign.
Key Points
- 1Trained a gait foundation model on 3,414 adults using depth-camera 3D skeletal motion across five tasks
- 2Demonstrated embeddings predict age (r=0.69), BMI (r=0.90), VAT area (r=0.82), outperforming engineered features
- 3Enable passive, scalable multi-system health monitoring and phenotype prediction from consumer-grade video
Scoring Rationale
Strong multi-system validation and large cohort support high impact, limited by preprint status and translation needs.
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