Context- and Terrain-aware Gait Analysis (CTAGA)

March 18, 2026 updated by: Professor Rory O'Connor, University of Leeds

Context- and Terrain-aware Gait Analysis and Visualisation

The average lifespan of individuals in many developed countries is increasing. This factor paired with the increase in global population has the potential to put a strain on healthcare systems with regards to age-related conditions. Particularly, this research considers the impact that conditions such as Parkinson's disease, dementia and stroke have on the walking capabilities on affected individuals.

This research project aims to obtain a gait analysis dataset consisting of sensor data captured during regular daily activities on common terrains such as grass, paving slabs, gravel, etc. The dataset will be collected with a custom sensor system which captures mobility data from a cohort of healthy controls of all ages and people with dementia, Parkinson's disease, stroke survivors, multiple sclerosis, etc. Various machine learning algorithms (custom-implemented using Python) will then be used to determine the walking activity (walking, ramp ascend/descend, stair ascend/descend etc.), the terrain (grass, pavement, carpet etc.), and various walking-related parameters (step length, step height, cadence etc.). It is our hope that these features will enable remote gait analysis to be performed with sufficient contextual information to enable remote diagnosis and rehabilitation tracking for those at risk of falling.

Study Overview

Status

Withdrawn

Conditions

Intervention / Treatment

Detailed Description

Walking is a crucial ability to allow people to live normal, healthy lives. However, various conditions which affect the brain or the body such as Parkinson's disease, dementia, stroke, multiple sclerosis, and amputations threaten a person's ability to walk and can lead to falls or the fear of falling. Either of these fall-related burdens can severely affect the quality of life for a person, particularly those who are most vulnerable, such as older people.

To detect fall-related issues in a person's manner of walking (their gait), a process called gait-analysis can be performed which involves a team of specialists using video cameras to record someone walking in a laboratory environment and analyse the video to identify problems. However, current technology is rapidly advancing towards the capacity for remote gait analysis, which uses wearable sensor technologies to capture one's gait. This provides many benefits such as a more natural walking style, automatic data analysis, and reduced time needed by specialists to perform the analysis. The largest of these benefits, however, is the capacity to wear the device outside of the laboratory to see how a person walks on real terrains.

Many current studies have shown great strides in producing highly accurate gait analysis systems. However, a real-environment dataset for these systems to be tested on does not yet exist. Furthermore, datasets including a range of people with conditions that increase their risk of falling are scarce and typically only focus on one group. This study aims to produce and analyse the first real-world gait analysis dataset which includes a wide range of gait-affecting conditions, and to highlight what worked and what didn't for future researchers to build off when designing and implementing practical solutions to real-environment gait analysis.

Study Type

Observational

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Adult
  • Older Adult

Accepts Healthy Volunteers

No

Sampling Method

Non-Probability Sample

Study Population

Patients attending the rehabilitation clinic at Chapel Allerton Hospital in Leeds, UK

Description

Inclusion Criteria:

  • Age > 18 years
  • Confirmed diagnosis of Parkinson's disease, dementia, acquired brain injury (stroke, cerebral palsy, etc.), multiple sclerosis, or have had a lower limb amputation.
  • Under the care of a Leeds consultant or specialist nurse
  • Able to walk indoors and outdoors without a walking aid

Exclusion Criteria:

  • Age < 18 years
  • Skin condition on feet, ankles, or waist that prevents wearing the sensor system.
  • Cognitive impairment causing inability to consent.
  • Unable to walk safely with provided shoes (need for specialist shoes).
  • Allergy to silicone or elastic fibres

Study Plan

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / Treatment
Gait analysis
Group whose gait is being analysed
Device: All-terrain Gait Analysis System
Mobile gait analysis system for environmental gait analysis

What is the study measuring?

Primary Outcome Measures

Outcome Measure
Measure Description
Time Frame
Number of participants to complete gait analysis using the All-terrain Gait Analysis System
Time Frame: From enrollment to the completion of the gait analysis.
The number of participants who are able to complete all elements of the All-terrain Gait Analysis System, which is a mobile gait analysis system to analyse gait in the environment, will be recorded to assess the feasibility of using the system in a larger observational study.
From enrollment to the completion of the gait analysis.

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

University of Leeds

Investigators

  • Principal Investigator: Rory J O'Connor, MD, University of Leeds

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Estimated)

March 1, 2025

Primary Completion (Estimated)

July 1, 2025

Study Completion (Estimated)

July 1, 2025

Study Registration Dates

First Submitted

November 15, 2024

First Submitted That Met QC Criteria

March 18, 2026

First Posted (Actual)

March 24, 2026

Study Record Updates

Last Update Posted (Actual)

March 24, 2026

Last Update Submitted That Met QC Criteria

March 18, 2026

Last Verified

March 1, 2026

More Information

Terms related to this study

Keywords

Additional Relevant MeSH Terms

Other Study ID Numbers

  • 306353

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

NO

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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