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

Lookup NU author(s): Dr Cameron KirkORCiD, Dr Paolo Tasca, Dr Metin BicerORCiD, Dr Dimitris Megaritis, Dr Chloe Hinchliffe, Professor Lynn RochesterORCiD, Dr Silvia Del DinORCiD

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

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Author(s): Kirk C, Kuederle A, Tasca P, Bicer M, Megaritis D, Gazit E, Bonci T, Ionescu A, Hinchliffe C, Stihi A, Muecke A, Babar Z, Vogiatzis I, Eskofier BM, Mazzà C, Cereatti A, Mueller A, Rooks D, Caulfield B, Rochester L, Del Din S

Publication type: Article

Publication status: Published

Journal: Sensors

Year: 2026

Volume: 26

Issue: 13

Online publication date: 06/07/2026

Acceptance date: 02/07/2026

Date deposited: 06/07/2026

ISSN (electronic): 1424-8220

Publisher: MDPI AG

URL: https://doi.org/10.3390/s26134294

DOI: 10.3390/s26134294

Data Access Statement: The datasets supporting the examples in this technical note can be found publicly available on Zenodo (https://zenodo.org/records/13899386, accessed 12 October 2024). The mobgap analytical pipeline is publicly available for use on Python via GitHub (https://mobgap.readthedocs.io/en/stable/, accessed 20 January 2026).

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