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- Title
A wearable sensor and machine learning estimate step length in older adults and patients with neurological disorders.
- Authors
Zadka, Assaf; Rabin, Neta; Gazit, Eran; Mirelman, Anat; Nieuwboer, Alice; Rochester, Lynn; Del Din, Silvia; Pelosin, Elisa; Avanzino, Laura; Bloem, Bastiaan R.; Della Croce, Ugo; Cereatti, Andrea; Hausdorff, Jeffrey M.
- 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.
- Subjects
PEARSON correlation (Statistics); RESEARCH funding; WEARABLE technology; GAIT in humans; PARKINSON'S disease; DESCRIPTIVE statistics; NEUROLOGICAL disorders; WALKING; AGING; COGNITION disorders; ANALYSIS of variance; MACHINE learning; OLD age
- Publication
NPJ Digital Medicine, 2024, Vol 7, Issue 1, p1
- ISSN
2398-6352
- Publication type
Article
- DOI
10.1038/s41746-024-01136-2