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Background <p>Stride time variability (STV) is an important indicator of gait stability and motor control during running, yet its assessment typically depends on laboratory-based systems or distal sensor placement. This study evaluated whether a single waist-mounted inertial measurement unit (IMU) can estimate STV during prolonged treadmill running using machine learning.</p> Method <p><?tk 4?>Eighteen healthy young adults completed 30&#xa0;min of treadmill running at self-selected speeds while IMUs were placed on the right dorsal foot and low back. Stride times were identified from the foot-mounted IMU and used to calculate STV as the coefficient of variation of 50 consecutive strides, which served as the reference outcome. From the waist-mounted IMU, time- and frequency-domain features were extracted from tri-axial acceleration and angular velocity signals. Four regression models, including artificial neural network (ANN), random forest (RF), decision tree (DT), and XGBoost, were developed to estimate STV. Model performance was evaluated using leave-one-subject-out cross-validation with root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²).</p> Results <p>All models demonstrated good subject-independent predictive ability, with XGBoost achieving the best overall performance (R² =0.942, MAE = 0.005, RMSE = 0.009). Features related to forward acceleration, particularly mean and dominant frequency in the X direction, were consistently among the most influential predictors.</p> Conclusion <p>These findings suggest that trunk kinematics recorded from a single waist-mounted IMU contain sufficient information to estimate running STV, supporting the feasibility of unobtrusive gait stability monitoring during endurance running with implications for assessing fall risk and rehabilitation progress.</p>

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Predicting stride time variability from waist-mounted IMU data using machine learning: toward non-invasive gait stability monitoring in endurance running

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年5月25日

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  • Shane Fei Chen,
  • Xianglin Zhao,
  • Jason Tak-Man Cheung,
  • Ming Zhang

摘要

Background

Stride time variability (STV) is an important indicator of gait stability and motor control during running, yet its assessment typically depends on laboratory-based systems or distal sensor placement. This study evaluated whether a single waist-mounted inertial measurement unit (IMU) can estimate STV during prolonged treadmill running using machine learning.

Method

Eighteen healthy young adults completed 30 min of treadmill running at self-selected speeds while IMUs were placed on the right dorsal foot and low back. Stride times were identified from the foot-mounted IMU and used to calculate STV as the coefficient of variation of 50 consecutive strides, which served as the reference outcome. From the waist-mounted IMU, time- and frequency-domain features were extracted from tri-axial acceleration and angular velocity signals. Four regression models, including artificial neural network (ANN), random forest (RF), decision tree (DT), and XGBoost, were developed to estimate STV. Model performance was evaluated using leave-one-subject-out cross-validation with root mean squared error (RMSE), mean absolute error (MAE), and coefficient of determination (R²).

Results

All models demonstrated good subject-independent predictive ability, with XGBoost achieving the best overall performance (R² =0.942, MAE = 0.005, RMSE = 0.009). Features related to forward acceleration, particularly mean and dominant frequency in the X direction, were consistently among the most influential predictors.

Conclusion

These findings suggest that trunk kinematics recorded from a single waist-mounted IMU contain sufficient information to estimate running STV, supporting the feasibility of unobtrusive gait stability monitoring during endurance running with implications for assessing fall risk and rehabilitation progress.