Training and Testing Database for IMU Based Gait Analysis Methods

ConditionHealthy Subjects or Volunteers

Trial statusRecruiting

Trial phaseNot listed

Trial typeObservational

Biological sexAll

Age18-65

SponsorVrije Universiteit Brussel

About this trial

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.

Eligibility criteria

This trial accepts healthy volunteers

Qualifiers

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.

Disqualifiers

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.

Trial population

Healthy Adults, age 18-65

Trial design

Design model

Cohort

Time perspective

Prospective

Treatments tested in this trial

Not listed

Trial groups

150 Participants

are grouped into 1 trial group

Group A: Healthy subjects

Trial outcomes

Primary outcomes

Heel-Strike Timing Error: Force Plates vs Vicon

Absolute time difference between heel-strike detected by synchronized force plates (reference) and heel-strike detected by the Vicon motion-capture system. Force-plate heel-strike is defined as the first frame with vertical ground-reaction force. Vicon heel-strike is the kinematic event labeled "heel-strike" per the lab's standard pipeline. Error is computed per step, then summarised as overall mean ± SD across participant(s). Lower values indicate better agreement. Unit of Measure: milliseconds (ms)

Time frame

4 years

Toe-Off Timing Error: Force Plates vs Vicon

Absolute time difference between toe-off from force plates and toe-off detected by Vicon. Error is computed per step, averaged within participant, then summarized as overall mean ± SD. Lower values indicate better agreement. Unit of Measure: milliseconds (ms)

Time frame

4 years

Event Agreement (%): Vicon vs Video - Heel-Strike

Percentage of heel-strike events for which Vicon and frame-by-frame video annotation indicate the same event within ms tolerance window (several tolerance windows will be explored). Agreement is computed per participant and summarized as mean ± SD across participant(s). Higher values indicate better agreement. Unit of Measure: percent (%)

Time frame

4 years

Event Agreement (%): Vicon vs Video - Toe-Off

Percentage of toe-off events for which Vicon and video annotation match within a tolerance window (several tolerance windows will be assessed). Computed per participant; summarized as mean ± SD per participant(s). Higher values indicate better agreement. Unit of Measure: percent (%)

Time frame

4 years

Secondary outcomes

IMU Heel-Strike Detection Accuracy

Proportion of correctly classified heel-strike events by the IMU-based deep learning model relative to Vicon ground truth (correct classifications / total events), computed per participant and summarized as mean ± SD across participant(s). Reference \& tolerance: Vicon event labels; a match is counted when IMU and Vicon events fall within same detection frame or within a tolerance window we will evaluate. Unit of Measure: percent (%)

Time frame

4 years

Step Time Accuracy

Description: Absolute difference in step time between IMU-derived and Vicon-derived gait event detection. Step time is defined as the time between consecutive heel-strikes of opposite feet. Unit of Measure: milliseconds (ms)

Time frame

4 years

IMU Toe-Off Detection Accuracy

Proportion of correctly classified toe off events by the IMU-based deep learning model relative to Vicon ground truth (correct classifications / total events), computed per participant and summarized as mean ± SD across participant(s). Reference \& tolerance: Vicon event labels; a match is counted when IMU and Vicon events fall within same detection frame or within a tolerance window we will evaluate. Unit of Measure: percent (%)

Time frame

4 years

IMU Heel-Strike Sensitivity (Recall)

True positives / (true positives + false negatives) for heel-strike detection vs Vicon, computed per participant(s); summarized as mean ± SD. Higher is better. Unit of Measure: percent (%) Reference \& tolerance: Vicon labels; match within a tolerance window to be analysed

Time frame

Other outcomes

Weight

Body weight will be measured using a calibrated digital scale, with participants wearing light clothing and no shoes. Weight will be recorded once at baseline and at scheduled follow-ups. Results will be summarized as mean ± standard deviation across participants. Unit of Measure: kilograms (kg)

Time frame

4 years

Height

Standing height will be measured using a wall-mounted stadiometer, with participants standing upright, without shoes, and heels together. Results will be summarized as mean ± standard deviation across participants. Unit of Measure: meters (mm)

Time frame

4 years

Body Mass Index (BMI)

BMI will be calculated as weight (kg) divided by height squared (m²). This provides a standardized measure of relative weight adjusted for height. Results will be summarized as mean ± standard deviation across participants. Unit of Measure: kilograms per square meter (kg/m²)

Time frame

4 years

Sponsors and contacts

Click on the lead sponsor to view all of their trials.

Vrije Universiteit Brussel

Lead sponsor

Source ClinicalTrials.gov

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