Context- and Terrain-aware Gait Analysis

NCT07491588 | Withdrawn

• 1 other identifier: 306353
• Study Type: observational
• Target: N/A
• Locations: 0 countries
• Sites: N/A

Brief Summary

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.

Conditions

Parkinson's Disease and Parkinsonism; Dementia; Acquired Brain Injury; Multiple Sclerosis; Lower Limb Amputation Below Knee (Injury)

Keywords

Gait analysis; Sensor system; Long-term health condition

Outcome Measures

Primary Outcomes (1)

• Number of participants to complete gait analysis using the All-terrain Gait Analysis System

  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.

Study Arms (1)

Gait analysis

Group whose gait is being analysed

Device: All-terrain Gait Analysis System

Interventions

All-terrain Gait Analysis System DEVICE

Mobile gait analysis system for environmental gait analysis

Gait analysis

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

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

You may qualify if:

• 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

You may not qualify if:

• 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

Sponsors & Collaborators

• University of Leeds: lead

MeSH Terms

Conditions

Parkinson Disease; Parkinsonian Disorders; Dementia; Brain Injuries; Multiple Sclerosis; Wounds and Injuries

Condition Hierarchy (Ancestors)

Basal Ganglia Diseases; Brain Diseases; Central Nervous System Diseases; Nervous System Diseases; Movement Disorders; Synucleinopathies; Neurodegenerative Diseases; Neurocognitive Disorders; Mental Disorders; Craniocerebral Trauma; Trauma, Nervous System; Demyelinating Autoimmune Diseases, CNS; Autoimmune Diseases of the Nervous System; Demyelinating Diseases; Autoimmune Diseases; Immune System Diseases

Study Officials

• Rory J O'Connor, MD

  University of Leeds

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Charterhouse Professor of Rehabilitation Medicine

Study Record Dates

• First Submitted: November 15, 2024
• First Posted: March 24, 2026
• Study Start: March 1, 2025
• Primary Completion: July 1, 2025
• Study Completion: July 1, 2025
• Last Updated: March 24, 2026

Record last verified: 2026-03

Data Sharing

• IPD Sharing: Will not share