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

backgroundStride 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.

methodEighteen 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).

resultsAll 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.

conclusionThese 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.