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A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders

Lookup NU author(s): Professor Lynn RochesterORCiD, Dr Silvia Del DinORCiD

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Licence

This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

© 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.


Publication metadata

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

Data Access Statement: The data analyzed in this study will be made available upon reasonable request and as allowed by human study committees. The underlying code for this study will be available at https://github.com/assafzadka/XGB-SLE/tree/main


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Funding

Funder referenceFunder name
ESPRC
EP/W031590/1

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