A Gait Foundation Model Predicts Multi-System Health Phenotypes from 3D Skeletal Motion
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arXiv
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| Autori principali: | , , , , , , , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2026
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| author | Gabet, Adam Kohn, Sarah Lutsker, Guy Gelman, Shira Godneva, Anastasia Sasson, Gil Zulti, Arad Krongauz, David Shaulitch, Rotem Rotem, Assaf Doron, Ohad Brodsky, Yuval Weinberger, Adina Segal, Eran |
| author_facet | Gabet, Adam Kohn, Sarah Lutsker, Guy Gelman, Shira Godneva, Anastasia Sasson, Gil Zulti, Arad Krongauz, David Shaulitch, Rotem Rotem, Assaf Doron, Ohad Brodsky, Yuval Weinberger, Adina Segal, Eran |
| contents | Gait is increasingly recognized as a vital sign, yet current approaches treat it as a symptom of specific pathologies rather than a systemic biomarker. We developed a gait foundation model for 3D skeletal motion from 3,414 deeply phenotyped adults, recorded via a depth camera during five motor tasks. Learned embeddings outperformed engineered features, predicting age (Pearson r = 0.69), BMI (r = 0.90), and visceral adipose tissue area (r = 0.82). Embeddings significantly predicted 1,980 of 3,210 phenotypic targets; after adjustment for age, BMI, VAT, and height, gait provided independent gains in all 18 body systems in males and 17 of 18 in females, and improved prediction of clinical diagnoses and medication use. Anatomical ablation revealed that legs dominated metabolic and frailty predictions while torso encoded sleep and lifestyle phenotypes. These findings establish gait as an independent multi-system biosignal, motivating translation to consumer-grade video and its integration as a scalable, passive vital sign. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2603_25283 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Gait Foundation Model Predicts Multi-System Health Phenotypes from 3D Skeletal Motion Gabet, Adam Kohn, Sarah Lutsker, Guy Gelman, Shira Godneva, Anastasia Sasson, Gil Zulti, Arad Krongauz, David Shaulitch, Rotem Rotem, Assaf Doron, Ohad Brodsky, Yuval Weinberger, Adina Segal, Eran Artificial Intelligence Quantitative Methods Gait is increasingly recognized as a vital sign, yet current approaches treat it as a symptom of specific pathologies rather than a systemic biomarker. We developed a gait foundation model for 3D skeletal motion from 3,414 deeply phenotyped adults, recorded via a depth camera during five motor tasks. Learned embeddings outperformed engineered features, predicting age (Pearson r = 0.69), BMI (r = 0.90), and visceral adipose tissue area (r = 0.82). Embeddings significantly predicted 1,980 of 3,210 phenotypic targets; after adjustment for age, BMI, VAT, and height, gait provided independent gains in all 18 body systems in males and 17 of 18 in females, and improved prediction of clinical diagnoses and medication use. Anatomical ablation revealed that legs dominated metabolic and frailty predictions while torso encoded sleep and lifestyle phenotypes. These findings establish gait as an independent multi-system biosignal, motivating translation to consumer-grade video and its integration as a scalable, passive vital sign. |
| title | A Gait Foundation Model Predicts Multi-System Health Phenotypes from 3D Skeletal Motion |
| topic | Artificial Intelligence Quantitative Methods |
| url | https://arxiv.org/abs/2603.25283 |