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Wearable devices can estimate step length continuously (e.g., in clinic or real-world settings), however, the accuracy of current estimation methods is not yet optimal. We developed machine-learning models to estimate step length based on data derived from a single lower-back inertial measurement unit worn by 472 young and older adults with different neurological conditions, including Parkinson\u2019s disease and healthy controls. Studying more than 80,000 steps, the best model showed high accuracy for a single step (root mean square error, RMSE\u2009=\u20096.08\u2009cm, ICC(2,1)\u2009=\u20090.89) and higher accuracy when averaged over ten consecutive steps (RMSE\u2009=\u20094.79\u2009cm, ICC(2,1)\u2009=\u20090.93), successfully reaching the predefined goal of an RMSE below 5\u2009cm (often considered the minimal-clinically-important-difference). Combining machine-learning with a single, wearable sensor generates accurate step length measures, even in patients with neurologic disease. Additional research may be needed to further reduce the errors in certain conditions.<\/jats:p>","DOI":"10.1038\/s41746-024-01136-2","type":"journal-article","created":{"date-parts":[[2024,5,25]],"date-time":"2024-05-25T06:01:39Z","timestamp":1716616899000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders"],"prefix":"10.1038","volume":"7","author":[{"given":"Assaf","family":"Zadka","sequence":"first","affiliation":[]},{"given":"Neta","family":"Rabin","sequence":"additional","affiliation":[]},{"given":"Eran","family":"Gazit","sequence":"additional","affiliation":[]},{"given":"Anat","family":"Mirelman","sequence":"additional","affiliation":[]},{"given":"Alice","family":"Nieuwboer","sequence":"additional","affiliation":[]},{"given":"Lynn","family":"Rochester","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1154-4751","authenticated-orcid":false,"given":"Silvia","family":"Del Din","sequence":"additional","affiliation":[]},{"given":"Elisa","family":"Pelosin","sequence":"additional","affiliation":[]},{"given":"Laura","family":"Avanzino","sequence":"additional","affiliation":[]},{"given":"Bastiaan R.","family":"Bloem","sequence":"additional","affiliation":[]},{"given":"Ugo","family":"Della Croce","sequence":"additional","affiliation":[]},{"given":"Andrea","family":"Cereatti","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1608-0776","authenticated-orcid":false,"given":"Jeffrey M.","family":"Hausdorff","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,25]]},"reference":[{"key":"1136_CR1","doi-asserted-by":"publisher","first-page":"697","DOI":"10.1016\/S1474-4422(19)30044-4","volume":"18","author":"A Mirelman","year":"2019","unstructured":"Mirelman, A. et al. 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Financial disclosures are disclosed below: Professors Mirelman and Hausdorff serve on the advisory board for the Michael J Fox Foundation for Parkinson\u2019s Research and F. Hoffmann-La Roche AG (JH) in areas relating to the use of wearable sensors to assess mobility in PD. Dr. Del Din reports consultancy activity with Hoffmann-La Roche Ltd. relating to the use of wearable sensors to assess mobility in PD, outside of this study. Prof. Bloem has received research grants from Verily Life Sciences and UCB for the development of smartwatch-based digital measures for Parkinson\u2019s disease. Prof Rochester serves as a consultant for the Michael J Fox Foundation for Parkinson\u2019s Research.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"142"}}