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
Further reading
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- Medicine
Background: Several fields have described low reproducibility of scientific research and poor accessibility in research reporting practices. Although previous reports have investigated accessible reporting practices that lead to reproducible research in other fields, to date, no study has explored the extent of accessible and reproducible research practices in cardiovascular science literature.
Methods: To study accessibility and reproducibility in cardiovascular research reporting, we screened 639 randomly selected articles published in 2019 in three top cardiovascular science publications: Circulation, the European Heart Journal, and the Journal of the American College of Cardiology (JACC). Of those 639 articles, 393 were empirical research articles. We screened each paper for accessible and reproducible research practices using a set of accessibility criteria including protocol, materials, data, and analysis script availability, as well as accessibility of the publication itself. We also quantified the consistency of open research practices within and across cardiovascular study types and journal formats.
Results: We identified that fewer than 2% of cardiovascular research publications provide sufficient resources (materials, methods, data, and analysis scripts) to fully reproduce their studies. Of the 639 articles screened, 393 were empirical research studies for which reproducibility could be assessed using our protocol, as opposed to commentaries or reviews. After calculating an accessibility score as a measure of the extent to which an article makes its resources available, we also showed that the level of accessibility varies across study types with a score of 0.08 for Case Studies or Case Series and 0.39 for Clinical Trials (p = 5.500E-5) and across journals (0.19 through 0.34, p = 1.230E-2). We further showed that there are significant differences in which study types share which resources.
Conclusion: Although the degree to which reproducible reporting practices are present in publications varies significantly across journals and study types, current cardiovascular science reports frequently do not provide sufficient materials, protocols, data, or analysis information to reproduce a study. In the future, having higher standards of accessibility mandated by either journals or funding bodies will help increase the reproducibility of cardiovascular research.
Funding: Authors Gabriel Heckerman, Arely Campos-Melendez, and Chisomaga Ekwueme were supported by an NIH R25 grant from the National Heart, Lung and Blood Institute (R25HL147666). Eileen Tzng was supported by an AHA Institutional Training Award fellowship (18UFEL33960207).
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- Cell Biology
- Medicine
Background:
It has been reported that loss of PCBP2 led to increased reactive oxygen species (ROS) production and accelerated cell aging. Knockdown of PCBP2 in HCT116 cells leads to significant downregulation of fibroblast growth factor 2 (FGF2). Here, we tried to elucidate the intrinsic factors and potential mechanisms of bone marrow mesenchymal stromal cells (BMSCs) aging from the interactions among PCBP2, ROS, and FGF2.
Methods:
Unlabeled quantitative proteomics were performed to show differentially expressed proteins in the replicative senescent human bone marrow mesenchymal stromal cells (RS-hBMSCs). ROS and FGF2 were detected in the loss-and-gain cell function experiments of PCBP2. The functional recovery experiments were performed to verify whether PCBP2 regulates cell function through ROS/FGF2-dependent ways.
Results:
PCBP2 expression was significantly lower in P10-hBMSCs. Knocking down the expression of PCBP2 inhibited the proliferation while accentuated the apoptosis and cell arrest of RS-hBMSCs. PCBP2 silence could increase the production of ROS. On the contrary, overexpression of PCBP2 increased the viability of both P3-hBMSCs and P10-hBMSCs significantly. Meanwhile, overexpression of PCBP2 led to significantly reduced expression of FGF2. Overexpression of FGF2 significantly offset the effect of PCBP2 overexpression in P10-hBMSCs, leading to decreased cell proliferation, increased apoptosis, and reduced G0/G1 phase ratio of the cells.
Conclusions:
This study initially elucidates that PCBP2 as an intrinsic aging factor regulates the replicative senescence of hBMSCs through the ROS-FGF2 signaling axis.
Funding:
This study was supported by the National Natural Science Foundation of China (82172474).
