Step Length Estimation Method Based on Residual Neural Network for Pedestrian Dead Reckoning with Shoulder-Mounted IMU

Pedestrian Dead Reckoning (PDR) technology demonstrates significant application value in smart city location services and IoT terminal positioning due to its signal-independent operation, autonomous navigation capability, and anti-interference advantages. However, existing shoulder-mounted inertial measurement units (IMUs) encounter gait characteristic modeling errors during practical deployment, particularly manifesting as nonlinear error accumulation caused by limited step-length prediction accuracy. To address this technical challenge, this study proposes a step-length estimation model based on residual neural networks (ResNet) with limited-sample training. The architecture achieves precise step-length prediction across various motion states through temporal feature extraction and multi-rate motion pattern analysis. Experimental results validated by five independent test sets demonstrate that the system achieves a relative displacement estimation error below 0.6%, with the mean absolute error (MAE) of single-step length prediction consistently remaining under 0.045 meters. Analytical verification confirms that the proposed step-length estimation method significantly enhances the step-length measurement accuracy of shoulder-mounted IMUs, providing an effective technical solution for high-precision indoor positioning of IoT devices.