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This paper presents a new constrained optimization-based smoothing algorithm for walking step length estimation using waist-mounted inertial sensors, where the total walking distance is known. The walking trajectory is estimated by double integrating acceleration. Due to sensor noises, the walking step length estimation accuracy degrades as the walking distance becomes longer. To tackle this problem, we introduce a known distance straight-line walking trajectory constraint and a constant speed constraint to the smoothing algorithm. These constraints reduce the walking step estimation accuracy degradation even for long walking distance. Two experiments are conducted to evaluate the pedestrian trajectory and walking step length estimation accuracy. The accuracy of a 20 m walking trajectory estimation has been investigated in the first experiment. This experiment compares the estimated position and velocity with Lidar-based references. The second experiment is to demonstrate the usefulness of the proposed walking step length estimation method. The result shows that the average of mean relative errors is 0.6801% for three different walking speed levels. The proposed method can be applied to generate training data for walking step length estimation without requiring spatial infrastructure.

Walking Step Length Estimation Using Waist-Mounted Inertial Sensors With Known Total Walking Distance