Fritz, S. & Lusardi, M. White paper: ‘walking speed: the sixth vital sign’. J. Geriatr. Phys. Ther. 32, 46–49 (2009).
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).
Guimarães, M. E. A. et al. Gait speed and incident depression: a systematic review and meta-analysis. J. Affect. Disord. 394, 120516 (2026).
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).
Cicirelli, G. et al. Human gait analysis in neurodegenerative diseases: a review. IEEE J. Biomed. Health Inform. 26, 229–242 (2022).
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).
Compagnat, M. et al. Oxygen cost during walking in individuals with stroke: hemiparesis versus cerebellar ataxia. Neurorehabil. Neural Repair 34, 289–298 (2020).
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).
Winner, T. S. et al. Discovering individual-specific gait signatures from data-driven models of neuromechanical dynamics. PLoS Comput. Biol. 19, e1011556 (2023).
Salchow-Hömmen, C. et al. Review—emerging portable technologies for gait analysis in neurological disorders. Front. Hum. Neurosci. 16, 768575 (2022).
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).
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).
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).
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).
Kohn, S. et al. Phenome-wide associations of sleep characteristics in the Human Phenotype Project. Nat. Med. 31, 1026–1037 (2025).
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).
López-Otín, C., Blasco, M. A., Partridge, L., Serrano, M. & Kroemer, G. Hallmarks of aging: an expanding universe. Cell 186, 243–278 (2023).
Neeland, I. J. et al. Visceral and ectopic fat, atherosclerosis, and cardiometabolic disease: a position statement. Lancet Diab. Endocrinol. 7, 715–725 (2019).
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).
Tang, G. H. & Sholzberg, M. Iron deficiency anemia among women: an issue of health equity. Blood Rev. 64, 101159 (2024).
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).
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).
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).
Stenum, J., Rossi, C. & Roemmich, R. T. Two-dimensional video-based analysis of human gait using pose estimation. PLOS Comput. Biol. 17, e1008935 (2021).
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).
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).
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).
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).
Revet, A. et al. Antidepressants and movement disorders: a postmarketing study in the world pharmacovigilance database. BMC Psychiatry 20, 308 (2020).
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).
Castera, L., Forns, X. & Alberti, A. Non-invasive evaluation of liver fibrosis using transient elastography. J. Hepatol. 48, 835–847 (2008).
Buergel, T. et al. Metabolomic profiles predict individual multidisease outcomes. Nat. Med. 28, 2309–2320 (2022).
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).
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).
Paillard, T. Detrimental effects of sleep deprivation on the regulatory mechanisms of postural balance: a comprehensive review. Front. Hum. Neurosci. 14, 1146550 (2023).
Umemura, G. S., Pinho, J. P., Duysens, J., Krebs, H. I. & Forner-Cordero, A. Sleep deprivation affects gait control. Sci. Rep. 11, 21104 (2021).
Kibler, W. B., Press, J. & Sciascia, A. The role of core stability in athletic function. Sports Med. 36, 189–198 (2006).
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).
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).
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).
Yamada, Y. et al. Utility of synthetic musculoskeletal gaits for generalizable healthcare applications. Nat. Commun. 16, 6188 (2025).
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).
Reicher, L. et al. Phenome-wide associations of human aging uncover sex-specific dynamics. Nat. Aging 4, 1643–1655 (2024).
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).
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).
Keshet, A. et al. CGMap: characterizing continuous glucose monitor data in thousands of non-diabetic individuals. Cell Metab. 35, 758–769 (2023).
Bycroft, C. et al. The UK Biobank resource with deep phenotyping and genomic data. Nature 562, 203–209 (2018).
Talmor-Barkan, Y. et al. Metabolomic and microbiome profiling reveals personalized risk factors for coronary artery disease. Nat. Med. 28, 295–302 (2022).
Su, J. et al. RoFormer: enhanced transformer with rotary position embedding. Neurocomputing 568, 127063 (2024).
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).
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).
