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Kirk, C.* ; Kuederle, A.* ; Tasca, P.* ; Bicer, M.* ; Megaritis, D.* ; Gazit, E.* ; Bonci, T.* ; Paraschiv-Ionescu, A.* ; Hinchliffe, C.* ; Stihi, A.* ; Muecke, A.* ; Babar, Z.* ; Vogiatzis, I.* ; Eskofier, B.M. ; Mazzà, C.* ; Cereatti, A.* ; Mueller, A.* ; Rooks, D.* ; Caulfield, B.* ; Rochester, L.* ; Din, S.D.*

Mobgap: A state-of-the-art python framework for reproducible estimation and algorithm validation of digital mobility outcomes from a single wearable device.

Sensors 26:4294 (2026)
Verlagsversion DOI
Open Access Gold
Creative Commons Lizenzvertrag
Objective, continuous assessment of real-world mobility using wearables has significant potential to transform clinical research and practice, yet the field lacks standardised, open-source tools that enable reproducible algorithm real-world validation, across multiple clinical cohorts. This would improve transparency around definitions and performance, thereby enhancing interpretation and more meaningful comparison across studies. The Mobilise-D consortium validated a comprehensive analytical pipeline for estimating digital mobility outcomes from wearables, originally implemented in a combination of MATLAB, R, and Python codes. To overcome the licencing, reproducibility, and accessibility limitations of this implementation, the pipeline has been re-implemented and re-validated, against gold standards, as the open-source mobgap Python package. Here, we describe the mobgap ecosystem, detail how algorithms can be integrated and benchmarked in a reproducible way and present a re-validation of the pipeline against reference data across six clinical cohorts under real-world conditions. Validation results showed that across all cohorts, walking speed was estimated with an absolute error of 0.10 m/s and an intraclass correlation coefficient (ICC) of 0.81, demonstrating comparable or superior performance to the original implementation. Mobgap (v1.2) is openly available and is intended to serve as a reproducible reference implementation and benchmarking platform for researchers developing or validating mobility analysis algorithms using wearable data.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Python (programming Language) ; Benchmarking ; Pipeline (software) ; Wearable Computer ; Intraclass Correlation ; Wearable Technology
ISSN (print) / ISBN 1424-8220
e-ISSN 1424-8220
Zeitschrift Sensors
Quellenangaben Band: 26, Heft: 13, Seiten: , Artikelnummer: 4294 Supplement: ,
Verlag MDPI
Begutachtungsstatus Peer reviewed
Institut(e) Institute of AI for Health (AIH)