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Lookup NU author(s): Professor Lynn RochesterORCiD, Dr Silvia Del DinORCiD
This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).
© The Author(s) 2024.Step length is an important diagnostic and prognostic measure of health and disease. 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’s 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 = 6.08 cm, ICC(2,1) = 0.89) and higher accuracy when averaged over ten consecutive steps (RMSE = 4.79 cm, ICC(2,1) = 0.93), successfully reaching the predefined goal of an RMSE below 5 cm (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.
Author(s): Zadka A, Rabin N, Gazit E, Mirelman A, Nieuwboer A, Rochester L, Del Din S, Pelosin E, Avanzino L, Bloem BR, Della Croce U, Cereatti A, Hausdorff JM
Publication type: Article
Publication status: Published
Journal: npj Digital Medicine
Year: 2024
Volume: 7
Issue: 1
Online publication date: 25/05/2024
Acceptance date: 10/05/2024
Date deposited: 10/06/2024
ISSN (electronic): 2398-6352
Publisher: Nature Research
URL: https://doi.org/10.1038/s41746-024-01136-2
DOI: 10.1038/s41746-024-01136-2
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