Abstract

Recently, head-worn inertial sensors have been proposed to characterize gait. However, only few methods allow for both initial foot contacts detection and stride-by-stride gait speed estimation, and none of them has been validated in real-world settings. In this study, we assessed the performance of a two-step machine learning algorithm to estimate initial foot contacts and speed in realworld conditions with a single inertial sensor attached to the temporal region of the head. A deep learning convolutional network is used to detect gait cycles. Then, gait speed is inferred from the detected gait cycles by a regression model. The models were trained and validated against a multisensor wearable system with data of 15 healthy young adults during both structured and real-world walking trials. The stride detector achieved a high F1-score (> 92%) and a mean absolute error smaller than 40 ms. High correlation between target and predicted speed values (Spearman coefficient > 0.86) and low mean absolute error (< 0.08 m/s) were observed. These findings pave the way to the establishment of gait analysis frameworks based on the integration of inertial sensors with head-worn devices.