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A gait foundation model for prediction of multisystem health phenotypes from 3D skeletal motion

A gait foundation model for prediction of multisystem health phenotypes from 3D skeletal motion

  • Fritz, S. & Lusardi, M. White paper: ‘walking speed: the sixth vital sign’. J. Geriatr. Phys. Ther. 32, 46–49 (2009).

    Article 
    PubMed 

    Google Scholar
     

  • Tucker, P. R. & Evans, D. D. Gait speed in the emergency department: improving assessment among older adults. Adv. Emerg. Nurs. J. 36, 209–214 (2014).

    Article 
    PubMed 

    Google Scholar
     

  • Guimarães, M. E. A. et al. Gait speed and incident depression: a systematic review and meta-analysis. J. Affect. Disord. 394, 120516 (2026).

    Article 
    PubMed 

    Google Scholar
     

  • Wang, Z., Peng, S., Zhang, H., Sun, H. & Hu, J. Gait parameters and peripheral neuropathy in patients with diabetes: a meta-analysis. Front. Endocrinol. 13, 891356 (2022).

    Article 

    Google Scholar
     

  • Cicirelli, G. et al. Human gait analysis in neurodegenerative diseases: a review. IEEE J. Biomed. Health Inform. 26, 229–242 (2022).

    Article 
    PubMed 

    Google Scholar
     

  • Zhang, W. et al. Wearable sensor-based quantitative gait analysis in Parkinson’s disease patients with different motor subtypes. npj Digital Med. 7, 169 (2024).

    Article 

    Google Scholar
     

  • Compagnat, M. et al. Oxygen cost during walking in individuals with stroke: hemiparesis versus cerebellar ataxia. Neurorehabil. Neural Repair 34, 289–298 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • van Schooten, K. S. et al. Daily-life gait quality as predictor of falls in older people: a 1-year prospective cohort study. PLOS ONE 11, e0158623 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Winner, T. S. et al. Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics. PLoS Comput. Biol. 19, e1011556 (2023).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Salchow-Hömmen, C. et al. Review—emerging portable technologies for gait analysis in neurological disorders. Front. Hum. Neurosci. 16, 768575 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Wren, T. A. L., Tucker, C. A., Rethlefsen, S. A., Gorton, G. E. & Õunpuu, S. Clinical efficacy of instrumented gait analysis: systematic review 2020 update. Gait Posture 80, 274–279 (2020).

    Article 
    PubMed 

    Google Scholar
     

  • Tan, T. et al. GaitDynamics: a generative foundation model for analyzing human walking and running. Nat. Biomed. Eng. 10, 1659–1671 https://doi.org/10.1038/s41551-025-01565-8 (2026).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Endo, M. et al. GaitForeMer: self-supervised pre-training of transformers via human motion forecasting for few-shot gait impairment severity estimation. Med. Image Comput. Comput. Assist. Interv. 13438, 130–139 (2022).

    PubMed 
    PubMed Central 

    Google Scholar
     

  • Cotton, R. J., et al. Self-supervised learning of gait-based biomarkers. In Proc. Predictive Intelligence in Medicine: 6th International Workshop (eds Rekik, I. et al.) 277–291 (Springer, 2023).

  • He, K. et al. Masked autoencoders are scalable vision learners. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 15979–15988 https://doi.org/10.1109/CVPR52688.2022.01553 (IEEE, 2022).

  • Wu, W. et al. Skeletonmae: spatial-temporal masked autoencoders for self-supervised skeleton action recognition. In 2023 IEEE International Conference on Multimedia and Expo Workshops (ICMEW) 224–229 https://doi.org/10.1109/ICMEW59549.2023.00045 (IEEE, 2023).

  • Reicher, L. et al. Deep phenotyping of health-disease continuum in the Human Phenotype Project. Nat. Med. 31, 3191–3203 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Kohn, S. et al. Phenome-wide associations of sleep characteristics in the Human Phenotype Project. Nat. Med. 31, 1026–1037 (2025).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Aune, D. et al. BMI and all cause mortality: systematic review and non-linear dose-response meta-analysis of 230 cohort studies with 3.74 million deaths among 30.3 million participants. Brit. Med. J. 353, i2156 (2016).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. Hallmarks of aging: an expanding universe. Cell 186, 243–278 (2023).

    Article 
    PubMed 

    Google Scholar
     

  • Neeland, I. J. et al. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Lancet Diab. Endocrinol. 7, 715–725 (2019).

    Article 

    Google Scholar
     

  • Pierrynowski, M. R. & Galea, V. Enhancing the ability of gait analyses to differentiate between groups: scaling gait data to body size. Gait Posture 13, 193–201 (2001).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Tang, G. H. & Sholzberg, M. Iron deficiency anemia among women: an issue of health equity. Blood Rev. 64, 101159 (2024).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Baker, R., Leboeuf, F., Reay, J. & Sangeux, M. The conventional gait model – success and limitations. in Handbook of Human Motion (eds Müller, B. & Wolf, S. I.) 489–508 https://doi.org/10.1007/978-3-319-14418-4_25 (Springer, 2018).

  • Studenski, S. et al. Gait speed and survival in older adults. JAMA 305, 50–58 (2011).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Prieto, T. E., Myklebust, J. B., Hoffmann, R. G., Lovett, E. G. & Myklebust, B. M. Measures of postural steadiness: differences between healthy young and elderly adults. IEEE Trans. Biomed. Eng. 43, 956–966 (1996).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Zhu, W. et al. Motionbert: a unified perspective on learning human motion representations. In Proc. IEEE/CVF International Conference on Computer Vision (ICCV) 15039–15053 https://doi.org/10.1109/ICCV51070.2023.01385 (IEEE, 2023).

  • Kidziński, Ł et al. Deep neural networks enable quantitative movement analysis using single-camera videos. Nat. Commun. 11, 4054 (2020).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Stenum, J., Rossi, C. & Roemmich, R. T. Two-dimensional video-based analysis of human gait using pose estimation. PLOS Comput. Biol. 17, e1008935 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Elguezabal Rodelo, R. G., Porchia, L. M., Torres-Rasgado, E., López-Bayghen, E. & Gonzalez-Mejia, M. E. Visceral and subcutaneous abdominal fat is associated with non-alcoholic fatty liver disease while augmenting metabolic syndrome’s effect on non-alcoholic fatty liver disease: a cross-sectional study of NHANES 2017–2018. PLOS ONE 19, e0298662 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Chakravarthy, M. V., Siddiqui, M. S., Forsgren, M. F. & Sanyal, A. J. Harnessing Muscle-liver crosstalk to treat nonalcoholic steatohepatitis. Front. Endocrinol. 11, 592373 (2020).

    Article 

    Google Scholar
     

  • Fudeyasu, K. et al. Advanced liver fibrosis is associated with decreased gait speed in older patients with chronic liver disease. Sci. Rep. 14, 6809 (2024).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Xia, N. et al. Relationship between metabolic dysfunction-associated steatotic liver disease and sarcopenia: a systematic review and meta-analyses. Clin. Nutr. ESPEN 68, 679–690 (2025).

    Article 
    PubMed 

    Google Scholar
     

  • Revet, A. et al. Antidepressants and movement disorders: a postmarketing study in the world pharmacovigilance database. BMC Psychiatry 20, 308 (2020).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Heinz, M. V. et al. Association of selective serotonin reuptake inhibitor use with abnormal physical movement patterns as detected using a piezoelectric accelerometer and deep learning in a nationally representative sample of noninstitutionalized persons in the US. JAMA Netw. Open 5, e225403 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Castera, L., Forns, X. & Alberti, A. Non-invasive evaluation of liver fibrosis using transient elastography. J. Hepatol. 48, 835–847 (2008).

    Article 
    PubMed 

    Google Scholar
     

  • Buergel, T. et al. Metabolomic profiles predict individual multidisease outcomes. Nat. Med. 28, 2309–2320 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Pencina, M. J., D’Agostino, R. B., D’Agostino, R. B. & Vasan, R. S. Evaluating the added predictive ability of a new marker: from area under the ROC curve to reclassification and beyond. Stat. Med. 27, 157–172 (2008).

    Article 
    PubMed 

    Google Scholar
     

  • Obermeyer, Z., Schubert, A., Ross, J., Mullainathan, S. & Lingman, M. An ECG biomarker for sudden cardiac death discovered with deep learning. Nature 655, 210–218 https://doi.org/10.1038/s41586-026-10674-6 (2026).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Paillard, T. Detrimental effects of sleep deprivation on the regulatory mechanisms of postural balance: a comprehensive review. Front. Hum. Neurosci. 14, 1146550 (2023).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Umemura, G. S., Pinho, J. P., Duysens, J., Krebs, H. I. & Forner-Cordero, A. Sleep deprivation affects gait control. Sci. Rep. 11, 21104 (2021).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Kibler, W. B., Press, J. & Sciascia, A. The role of core stability in athletic function. Sports Med. 36, 189–198 (2006).

    Article 
    PubMed 

    Google Scholar
     

  • Zemková, E. & Zapletalová, L. The role of neuromuscular control of postural and core stability in functional movement and athlete performance. Front. Physiol. 13, 796097 (2022).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Goodpaster, B. H. et al. The loss of skeletal muscle strength, mass, and quality in older adults: the health, aging and body composition study. J. Gerontol. A 61, 1059–1064 (2006).

    Article 

    Google Scholar
     

  • Schalkamp, A.-K., Peall, K. J., Harrison, N. A. & Sandor, C. Wearable movement-tracking data identify Parkinson’s disease years before clinical diagnosis. Nat. Med. 29, 2048–2056 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Yamada, Y. et al. Utility of synthetic musculoskeletal gaits for generalizable healthcare applications. Nat. Commun. 16, 6188 (2025).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Zheng, J. et al. Gait recognition in the wild with dense 3D representations and a benchmark. In 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (eds Dana, K. et al.) 20196–20205 https://doi.org/10.1109/CVPR52688.2022.01959 (IEEE, 2022).

  • Zhu, Z. et al. Gait recognition in the wild: a benchmark. In 2021 IEEE/CVF International Conference on Computer Vision (ICCV) (eds Berg, T. et al.) 14769–14779 https://doi.org/10.1109/ICCV48922.2021.01452 (IEEE, 2021).

  • Schneider, T. R. et al. Hand dexterity and mobility independently predict cognition in older adults: a multi-domain regression analysis. Front. Aging Neurosci. 17, 1624307 (2025).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Reicher, L. et al. Phenome-wide associations of human aging uncover sex-specific dynamics. Nat. Aging 4, 1643–1655 (2024).

    Article 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Rothschild, D. et al. An atlas of robust microbiome associations with phenotypic traits based on large-scale cohorts from two continents. PLOS ONE 17, e0265756 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Leviatan, S., Shoer, S., Rothschild, D., Gorodetski, M. & Segal, E. An expanded reference map of the human gut microbiome reveals hundreds of previously unknown species. Nat. Commun. 13, 3863 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Keshet, A. et al. CGMap: characterizing continuous glucose monitor data in thousands of non-diabetic individuals. Cell Metab. 35, 758–769 (2023).

    Article 
    CAS 
    PubMed 

    Google Scholar
     

  • Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Talmor-Barkan, Y. et al. Metabolomic and microbiome profiling reveals personalized risk factors for coronary artery disease. Nat. Med. 28, 295–302 (2022).

    Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar
     

  • Su, J. et al. RoFormer: enhanced transformer with rotary position embedding. Neurocomputing 568, 127063 (2024).

    Article 

    Google Scholar
     

  • Dao, T., Fu, D., Ermon, S., Rudra, A. & Ré, C. FlashAttention: fast and memory-efficient exact attention with IO-awareness. In Advances in Neural Information Processing Systems Vol. 35 (eds Koyejo, S. et al.) 16344–16359 (Curran Associates, 2022).

  • Paszke, A. et al. PyTorch: an imperative style, high-performance deep learning library. In Advances in Neural Information Processing Systems Vol. 32 (eds Wallach, H. et al.) 8024–8035 (Curran Associates, 2019).

  • Loshchilov, I. & Hutter, F. Decoupled weight decay regularization. In 7th International Conference on Learning Representations (Curran Associates, 2019).

  • Loshchilov, I. & Hutter, F. SGDR: stochastic gradient descent with warm restarts. In 5th International Conference on Learning Representations (Curran Associates, 2017).

  • Micikevicius, P. et al. Mixed precision training. In 6th International Conference on Learning Representations (Curran Associates, 2018).

  • von Luxburg, U. In Proc. 31st Annual Conference on Neural Information Processing Systems (eds Von Luxburg, U. et al.) 3149–3157 (Curran Associates, 2017).

  • Pedregosa, F. et al. Scikit-learn: machine learning in Python. Preprint at https://doi.org/10.48550/arxiv.1201.0490 (2012).

  • Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Series B 57, 289–300 (1995).

    Article 

    Google Scholar
     

  • Zeiler, M. D. & Fergus, R. Visualizing and understanding convolutional networks. In Proc. Computer Vision – ECCV 2014 (eds Fleet, D. et al.) 818–833 (Springer, 2014).

  • Gabet, A. AdamGabet/GaitPredict: foundation model for gait source code. Zenodo https://doi.org/10.5281/zenodo.22746741 (2026).

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