Fully automated gait analysis with earables: Evaluation of an End2End pipeline with hearing-aid integrated accelerometers.

Abstract

Earables, due to their unobtrusive and lightweight nature, are increasingly being recognized for their potential in estimating digital biomarkers, yet their application in gait analysis (GA) remains limited because comprehensive analytic tools are missing. Existing ear-worn systems have primarily addressed isolated aspects such as gait classification, stride time, or step length estimation, lacking a full end-to-end pipeline. Such pipelines are essential for efficient and automated workflows and real-world applications. This work presents a complete end-to-end GA pipeline for ear-worn accelerometers incorporating multiple algorithms to process raw sensor signals into spatio-temporal parameters. This multi-step approach includes gait sequence detection, event identification, and parameter estimation. We introduce a novel gait sequence detector (GSD) that automatically detects regions of interest in continuous recordings. The integrated spatio-temporal algorithms have already been validated in an isolated setting as part of a previous evaluation study. Using a dataset with three walking speeds and foot-worn IMUs as references, the GSD effectively detects 91 % of gait sequences. The pipeline achieves stride time and SL errors of around 4 % and a gait velocity error of 5.7 %, consistent with prior evaluation for the individual isolated steps. To our knowledge, this is the first end-to-end GA pipeline for earables. Furthermore, the pipeline was released as open-source toolbox (https://github.com/mad-lab-fau/eargait), to facilitate research access and reusability. Our work lays the foundation for automated, continuous, and long-term mobility assessment in home environments using lightweight, unobtrusive earables.