ORCID
- Lisa Bunn: 0000-0002-7263-3048
- Jennifer Freeman: 0000-0002-4072-9758
- Jeremy Hobart: 0000-0002-2114-7920
- Jonathan Marsden: 0000-0002-2037-4902
Abstract
Background: Many attempts to validate gait pipelines that process sensor data to detect gait events have focused on the detection of initial contacts only in supervised settings using a single sensor. Objective: To evaluate the performance of a gait pipeline in detecting initial/final contacts using a step detection algorithm adaptive to different test settings, smartphone wear locations, and gait impairment levels. Methods: In GaitLab (ISRCTN15993728), healthy controls (HC) and people with multiple sclerosis (PwMS; Expanded Disability Status Scale 0.0-6.5) performed supervised Two-Minute Walk Test [2MWT] (structured in-lab overground and treadmill 2MWT) during two on-site visits carrying six smartphones and unsupervised walking activities (structured and unstructured real-world walking) daily for 10-14 days using a single smartphone. Reference gait data were collected with a motion capture system or Gait Up sensors. The pipeline's performance in detecting initial/final contacts was evaluated through F1 scores and absolute temporal error with respect to reference measurement systems. Results: We studied 35 HC and 93 PwMS. Initial/final contacts were accurately detected across all smartphone wear locations. Median F1 scores for initial/final contacts on in-lab 2MWT were >=98.2%/96.5% in HC and >=98.5%/97.7% in PwMS. F1 scores remained high on structured (HC: 100% [0.3%]/100% [0.2%]; PwMS: 99.5% [1.9%]/99.4% [2.5%]) and unstructured real-world walking (HC: 97.8% [2.6%]/97.8% [2.8%]; PwMS: 94.4% [6.2%]/94.0% [6.5%]). Median temporal errors were <=0.08 s. Neither age, sex, disease severity, walking aid use, nor setting (outdoor/indoor) impacted pipeline performance (all p>0.05). Conclusion: This gait pipeline accurately and consistently detects initial and final contacts in PwMS across different smartphone locations and environments, highlighting its potential for real-world gait assessment.
DOI Link
Publication Date
2025-09-17
Publisher
arXiv
Acceptance Date
2025-09-17
Deposit Date
2025-09-23
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License
First Page
1
Last Page
1
Recommended Citation
Angelini, L., Stanev, D., Plonka, M., Kilmas, R., Naplorkowski, N., Gonzalez Chan, G., Bunn, L., Glazier, P., Hosking, R., Freeman, J., Hobart, J., Marsden, J., Craveiro, L., Rinderknecht, M., & Zanon, M. (2025) 'Adaptive and robust smartphone-based step detection in multiple sclerosis', arXiv: Available at: 10.48550/arXiv.2509.13961
