Digital wearable insole-based identification of knee arthropathies and gait signatures using machine learning
- Regeneron, United States
Abstract
Gait is impaired in musculoskeletal conditions, such as knee arthropathy. Gait analysis is used in clinical practice to inform diagnosis and to monitor disease progression or intervention response. However, clinical gait analysis relies on subjective visual observation of walking, as objective gait analysis has not been possible within clinical settings due to the expensive equipment, large-scale facilities, and highly trained staff required. Relatively low-cost wearable digital insoles may offer a solution to these challenges. In this work, we demonstrate how a digital insole measuring osteoarthritis-specific gait signatures yields similar results to the clinical gait-lab standard. To achieve this, we constructed a machine learning model, trained on force plate data collected in participants with knee arthropathy and controls. This model was highly predictive of force plate data from a validation set (area under the receiver operating characteristics curve [auROC] = 0.86; area under the precision-recall curve [auPR] = 0.90) and of a separate, independent digital insole dataset containing control and knee osteoarthritis subjects (auROC = 0.83; auPR = 0.86). After showing that digital insole derived gait characteristics are comparable to traditional gait measurements, we next showed that a single stride of raw sensor time series data could be accurately assigned to each subject, highlighting that individuals using digital insoles can be identified by their gait characteristics. This work provides a framework for a promising alternative to traditional clinical gait analysis methods, adds to the growing body of knowledge regarding wearable technology analytical pipelines, and supports clinical development of at-home gait assessments, with the potential to improve the ease, frequency, and depth of patient monitoring.
Data availability
Anonymized data and computer code to reproduce all figures will be made available as supplementary files to this manuscript. All relevant demographic and clinical information, all vGRF, derived gait characteristics, and raw sensor time series data, will be provided, in addition to R and Python scripts used to perform the analysis. Additionally, this information will be uploaded to a Regeneron GitHub account here: https://github.com/regeneron-mpds. This data will be made fully available prior to final publication of this manuscript. The GaitRec dataset is available online here: https://www.nature.com/articles/s41597-020-0481-z
Article and author information
Author details
Matthew F Wipperman
Funding
Regeneron Pharmaceuticals (N/A)
- Matthew F Wipperman
- Allen Z Lin
- Kaitlyn M Gayvert
- Benjamin Lahner
- Selin Somersan-Karakaya
- Xuefang Wu
- Joseph Im
- Minji Lee
- Bharatkumar Koyani
- Ian Setliff
- Malika Thakur
- Daoyu Duan
- Aurora Breazna
- Fang Wang
- Wei Keat Lim
- Gabor Halasz
- Jacek Urbanek
- Yamini Patel
- Gurinder S Atwal
- Jennifer D Hamilton
- Oren Levy
- Andreja Avbersek
- Rinol Alaj
- Sara C Hamon
- Olivier Harari
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: For the pilot study, participants were recruited internally within the Regeneron facility located in Tarrytown, NY, USA, and were provided written informed consent prior to participation. The study was considered exempt research under the Common Rule (45 CFR Sec 46.104). The R5069-OA-1849 study protocol received Institutional Review Board and ethics committee approvals from Moldova Medicines and Medical Device Agency and National Ethics Committee for Moldova, and the Western Institutional Review Board.
Copyright
© 2024, Wipperman et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Digital wearable insole-based identification of knee arthropathies and gait signatures using machine learning
eLife 13:e86132.
https://doi.org/10.7554/eLife.86132
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