Gait Parameters Analysis Based on Leg-and-shoe-mounted IMU and Deep Learning

We propose a wearable system and a chain of methods for estimating stride lengths from inertial measurement units (IMU). The data are first processed by a Long Short-Term Memory (LSTM)-based method to determine the timing of step events. The raw IMU data and extracted features are also fed to LSTM to construct a regression model for learning stride lengths. Experimental results show that the proposed step event detector can reach −0.015 s to 0.02 s accuracy, and the proposed precision of stride-length estimator achieves 3.8 cm for both the mechanical model and LSTM model with extracted features.