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

Assaf Zadka, Neta Rabin, Eran Gazit, Anat Mirelman, Alice Nieuwboer, Lynn Rochester, Silvia Del Din, Elisa Pelosin, Laura Avanzino, Bastiaan R. Bloem, Ugo Della Croce, Andrea Cereatti, Jeffrey M. Hausdorff*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish
Article number142
Journalnpj Digital Medicine
Volume7
Issue number1
DOIs
StatePublished - Dec 2024

Funding

FundersFunder number
NIHR Imperial Biomedical Research Centre
Israel Innovation Authority
Israel Science Foundation
European Federation of Pharmaceutical Industries and Associations
NIHR Newcastle Biomedical Research Centre
NHS Foundation Trust
Horizon 2020 Framework Programme
CNTW
European Commission
ONPar
Choroideremia Research Foundation
MS-Watch
Northumberland and Tyne and Wear
Wellcome Trust Clinical Research Facility
Newcastle University
Newcastle upon Tyne Hospitals NHS Foundation Trust
Tel Aviv University
Engineering and Physical Sciences Research Council
UK Research and Innovation
Seventh Framework ProgrammeV-TIME-278169
Seventh Framework Programme
Innovative Medicines Initiative820820
Innovative Medicines Initiative
IMI2 JU853981
National Institute for Health and Care ResearchEP/W031590/1
National Institute for Health and Care Research

    Fingerprint

    Dive into the research topics of 'A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders'. Together they form a unique fingerprint.

    Cite this