Parkinson's Disease and ParkinsonismCTAGANCT07491588
Context- and Terrain-aware Gait Analysis
An observational study of Parkinson's Disease and Parkinsonism and Dementia, run by University of Leeds.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Parkinson's Disease and Parkinsonism, Dementia, Acquired Brain Injury
- What is being tested?
- All-terrain Gait Analysis System
- Type of treatment
- Medical device
- How many people
- 0 participants
- Where
- No sites listed yet
- Study dates
- 1 Mar 2025to Jul 2025
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients attending the rehabilitation clinic at Chapel Allerton Hospital in Leeds, UK
Who may be able to join
- 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
Who cannot join
- 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
About this study
Official title: 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.
Read the 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.
- Conditions
- Parkinson's Disease and Parkinsonism, Dementia, Acquired Brain Injury, Multiple Sclerosis, Lower Limb Amputation Below Knee (Injury)
- Being tested
- All-terrain Gait Analysis System
- Keywords
- Gait analysis, Sensor system, Long-term health condition
- Medical subject headings
- Parkinson Disease, Parkinsonian Disorders, Dementia, Brain Injuries, Multiple Sclerosis, Wounds and Injuries
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
All-terrain Gait Analysis SystemDevice
Mobile gait analysis system for environmental gait analysis
| Study group | Role | What they receive |
|---|---|---|
| Gait analysis | Group whose gait is being analysed Receives: Device: All-terrain Gait Analysis System |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: From enrollment to the completion of the gait analysis.
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted24 Mar 2026
- Study start1 Mar 2025 (estimated)
- Primary completionJul 2025 (estimated)
- Study completionJul 2025 (estimated)
Why it stopped: The researcher working on this project left the project team to take up a role elsewhere.
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Parkinson's Disease and ParkinsonismStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 0 participants (actual)
Sponsor and investigators
- Lead sponsor
- University of Leeds
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Professor Rory O'Connor, University of Leeds
- Investigators
- Rory J O'Connor, MD, Principal investigator, University of Leeds
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07491588
- Sponsor's protocol ID
- 306353
- Acronym
- CTAGA
- Status last verified
- Mar 2026
- First submitted
- 15 Nov 2024
- Last updated
- 24 Mar 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 24 Mar 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Context- and Terrain-aware Gait Analysis and Visualisation
- ClinicalTrials.gov ID
- NCT07491588
- Acronym / sponsor ID
- CTAGA / 306353
- 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
- Study type
- Observational
- Recruitment status
- WithdrawnReason stopped: The researcher working on this project left the project team to take up a role elsewhere.
- Enrolment
- 0 participants (actual)
- Lead sponsor
- University of LeedsUniversity, hospital or other
- Study start
- 1 Mar 2025 (estimated)
- Primary completion
- Jul 2025 (estimated)
- Study completion
- Jul 2025 (estimated)
- First posted
- 24 Mar 2026 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Gait analysis | – | Device: All-terrain Gait Analysis SystemGroup whose gait is being analysed |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients attending the rehabilitation clinic at Chapel Allerton Hospital in Leeds, UK
Key inclusion criteria (4 listed)
- 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
Key exclusion criteria (5 listed)
- 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
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| All-terrain Gait Analysis SystemMobile gait analysis system for environmental gait analysis | Device | Gait analysis |
Primary outcome
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.
Results and safety
The study was withdrawn before enrolling participants, so there are no results.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 24 Mar 2026.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Rory J O'Connor, MD principal investigator
University of Leeds
Questions about this trial
What is this clinical trial studying?
An observational study of Parkinson's Disease and Parkinsonism and Dementia, run by University of Leeds.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: withdrawn, as of the sponsor's last update on 24 Mar 2026. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study was withdrawn before enrolling participants, so there are no results.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
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.). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 24 Mar 2026.