Training and Testing Database for IMU Based Gait Analysis Methods

The goal of this study is to establish a high-quality, synchronised dataset of gait events (GE) by simultaneously collecting inertial measurement unit (IMU) data and validated ground truth detections using a Vicon motion capture system. The primary objective is to address existing limitations in GE detection - such as poor generalisability, limited data diversity, and lack of precise synchronisation - through a rigorous protocol that ensures accuracy and transparency. The experiment is structured in three phases. First, Vicon-derived GE will be validated and refined using complementary modalities (force plates and video recordings). Next, deep learning (DL) algorithms will be developed and evaluated for GE detection directly from IMU data, with Vicon annotations serving as ground truth. Finally, the impact of differences in GE timing on spatiotemporal gait parameters (SGP) will be analys