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Smartwatch-Based Prediction of Single-Stride and Stride-to-Stride Gait Outcomes Using Regression-Based Machine Learning

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Abstract

Spatiotemporal variability during gait is linked to fall risk and could be monitored using wearable sensors. Although many users prefer wrist-worn sensors, most applications position at other sites. We developed and evaluated an application using a consumer-grade smartwatch inertial measurement unit (IMU). Young adults (n = 41) completed seven-minute conditions of treadmill gait at three speeds. Single-stride outcomes (stride time, length, width, and speed) and spatiotemporal variability (coefficient of variation of each single-stride outcome) were recorded using an optoelectronic system, while 232 single- and multi-stride IMU metrics were recorded using an Apple Watch Series 5. These metrics were input to train linear, ridge, support vector machine (SVM), random forest, and extreme gradient boosting (xGB) models of each spatiotemporal outcome. We conducted Model × Condition ANOVAs to explore model sensitivity to speed-related responses. xGB models were best for single-stride outcomes [relative mean absolute error (% error): 7–11%; intraclass correlation coefficient (ICC2,1) 0.60–0.86], and SVM models were best for spatiotemporal variability (% error: 18–22%; ICC2,1 = 0.47–0.64). Spatiotemporal changes with speed were captured by these models (Condition: p < 0.00625). Results support the feasibility of monitoring single-stride and multi-stride spatiotemporal parameters using a smartwatch IMU and machine learning.

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Acknowledgements

The authors thank the participants for volunteering their time.

Funding

This work was supported by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC), by the Ontario Ministry of Research, Innovation and Science Early Researcher Award, by postdoctoral fellowships from NSERC and the uOttawa-Children’s Hospital of Eastern Ontario Research Institute, and by the Apple Investigator Support Program.

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  1. School of Human Kinetics, University of Ottawa, Ottawa, Canada

    Christopher A. Bailey, Alexandre Mir-Orefice, Julie Nantel & Ryan B. Graham

  2. Department of Mechanical Engineering, University of Ottawa, Ottawa, Canada

    Thomas K. Uchida

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Correspondence to Ryan B. Graham.

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Apple Inc. supplied the smartwatches used in this study as part of the Investigator Support Program. Apple Inc. and funding sources had no involvement in study design, data collection, analysis, and interpretation, or writing of the manuscript. The authors have no other conflicts of interest to declare.

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Bailey, C.A., Mir-Orefice, A., Uchida, T.K. et al. Smartwatch-Based Prediction of Single-Stride and Stride-to-Stride Gait Outcomes Using Regression-Based Machine Learning. Ann Biomed Eng 51, 2504–2517 (2023). https://doi.org/10.1007/s10439-023-03290-2

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