Accurate Stride Length Prediction from Proximal IMU Sensors Using a Compact Linear Model

Abstract Objective Accurate stride length measurement is essential for assessing functional mobility, yet gold-standard methods remain confined to laboratory settings. This study aimed to develop and validate a computationally efficient, interpretable linear model for predicting stride length using thigh- and shank-mounted inertial measurement units integrated into a wearable neuromodulation sleeve. Methods Data from the sleeve were collected from 29 healthy adults performing walking bouts at four self-selected speeds. Participants traversed a pressure-sensitive gait mat, providing gold standard labels. A linear regression model was developed from engineered features from the kinematics data streams and validated against a held-out test set ( n = 6) using leaveone-participant-out cross-validation. Results The final linear model utilized five predictors: participant height, shank range of motion (ROM), thigh ROM, and thigh swing duration metrics. It achieved high predictive accuracy with a mean absolute error (MAE) of 5.98 cm, a mean absolute percentage error (MAPE) of 4.53%, and an R 2 of 0.89. The model significantly outperformed naive baseline models ( p < 0.05) and performed similarly to more complex non-linear architectures, such as neural networks and random forests. Notably, 88.4% of strides were predicted within 10% of the ground truth. Conclusion A parsimonious linear model leveraging proximal limb kinematics provides accurate and biomechanically interpretable stride length estimation. Low computational demand makes it suitable for real-time, ondevice gait monitoring in wearable assistive technologies, facilitating clinical assessments in real-world environments.