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

Navigation

Home

Deutsch

Research

Advanced Search

Browse by ...

... Journal

... Publication Type

... Research Data

... Publication Year

Publication overview

Support & Contact

Contact persons

Help

Data protection

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)

Publ. Version/Full Text

DOI

	Open Access Gold

Abstract
Metrics
Extra information

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.

Altmetric

Additional Metrics?

[➜Log in]

Edit extra informations Login

Publication type Article: Journal article

Document type Scientific Article

Keywords Python (programming Language) ; Benchmarking ; Pipeline (software) ; Wearable Computer ; Intraclass Correlation ; Wearable Technology; Inertial Sensor; Gait Parameters; Walking

ISSN (print) / ISBN 1424-8220

e-ISSN 1424-8220

Journal Sensors

Quellenangaben Volume: 26, Issue: 13, Article Number: 4294

Publisher MDPI

Publishing Place Mdpi Ag, Grosspeteranlage 5, Ch-4052 Basel, Switzerland

Reviewing status Peer reviewed

Institute(s) Institute of AI for Health (AIH)

Grants European Federation of Pharmaceutical Industries and Associations (EFPIA)
Innovative Medicines Initiative
Engineering and Physical Sciences Research Council (EPSRC)
National Institute for Health and Care Research (NIHR)
Cumbria, Northumberland and Tyne and Wear (CNTW) NHS Foundation Trust
European Union
Newcastle upon Tyne Hospitals NHS Foundation Trust
Newcastle University
UK Research and Innovation (UKRI)