Training and Testing Database for IMU Based Gait Analysis Methods
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- This is an observational study: the protocol does not assign a study treatment.
- Who it may be relevant to
- Registry conditions: Healthy Subjects or Volunteers. Basic parameters: 18 years — 65 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
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 analysed to assess the feasibility of using IMU-only systems for reliable gait analysis. By achieving these objectives, the study aims to improve the accuracy of GE detection from wearable sensors and enable more accessible, scalable, and reliable gait analysis outside the laboratory environment.
Detailed description
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.
Primary outcome measures
- Heel-Strike Timing Error: Force Plates vs Vicon [Time frame: 4 years]
- Toe-Off Timing Error: Force Plates vs Vicon [Time frame: 4 years]
- Event Agreement (%): Vicon vs Video - Heel-Strike [Time frame: 4 years]
- Event Agreement (%): Vicon vs Video - Toe-Off [Time frame: 4 years]
Secondary outcome measures (12)
- IMU Heel-Strike Detection Accuracy [Time frame: 4 years]
- Step Time Accuracy [Time frame: 4 years]
- IMU Toe-Off Detection Accuracy [Time frame: 4 years]
- IMU Heel-Strike Sensitivity (Recall) [Time frame: 4]
- IMU Heel-Strike Specificity [Time frame: 4 years]
- IMU Toe-Off Sensitivity (Recall) [Time frame: 4 years]
- IMU Toe-Off Specificity [Time frame: 4 years]
- Cadence Accuracy [Time frame: 4 years]
- Step Length Accuracy [Time frame: 4 years]
- Step-Time Asymmetry Accuracy [Time frame: 4 years]
- Stride Time Accuracy [Time frame: 4 years]
- Stride Length Accuracy [Time frame: 4 years]
Eligibility criteria
Inclusion criteria
- Healthy subjects with no motor impairments that disrupt the walking pattern.
- No history of pain in the lower limbs in the past 6 months.
- No history of lower limbs injuries/surgeries in the past 6 months.
- Language: Dutch and/or English and/or French speakers.
- Age 18-65 years old
- Subjects must be able to understand the instructions and to answer questions. Additionally, they should be able to; signal pain, fear, discomfort; give inform consent.
Exclusion criteria
- Persons with comorbidity that could hinder the study (e.g.: unstable cardiovascular system disorders, lung disorders, severe osteoporosis).
- Individuals with metal implants or skin conditions that would make sensor or marker placement difficult.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Observational model
- Cohort
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT07236008 · 25235_IMU-GAIT · 25235