Training and Testing Database for IMU Based Gait Analysis Methods

This project investigates the development of accurate and reliable gait event (GE) detection methods using wearable inertial measurement units (IMUs), validated against goldstandard motion capture data (Vicon). Gait analysis plays a central role in understanding human locomotion and has important clinical applications in rehabilitation, neurology, orthopaedics, and fall-risk assessment. However, current IMU-based approaches are limited by synchronisation issues, small or homogeneous datasets, and insufficient validation against ground truth. This study addresses these gaps by systematically collecting and validating gait data in healthy participants. Data collection will be performed at the Brubotics Rehabilitation Research Center (BRRC) motion analysis laboratory. Participants will complete walking trials at different speeds (slow, self-selected, and fast speeds) along a standardised 10 m pathway. Reflective markers will be placed on anatomical landmarks, and a sacrum-mounted IMU will capture inertial signals. GE will be simultaneously recorded with the Vicon system, complemented by video and force plate data for validation. The study is organised into three phases. Phase 1 validates and refines Vicon-detected GE using complementary modalities. Phase 2 develops and evaluates deep learning algorithms for IMU-based detection, including the exploration of self-supervised learning. Phase 3 examines how differences in GE timing influence spatiotemporal gait parameters (e.g., step time, cadence, asymmetry), with the goal of establishing whether IMU-only systems can serve as reliable alternatives to motion capture. Ultimately, this project will deliver a robust dataset and algorithmic framework that improve the precision and generalisability of IMU-based GE detection.