Gait DisordersNCT07003087
2D Video Gait Analysis Platform Applying to AI Model for Adjustment of the Shoes' Sole
An observational study of Gait Disorders, run by China Medical University Hospital at 1 site in Taiwan.
Trial summary
Artificial intelligence (AI) technology makes gait analysis based on two-dimensional images feasible. The first aim of this study is to develop an analysis platform that uses two-dimensional image analysis technology to determine the relative excursion of body segments and limbs in gait. Read more about this study
- Who is it for?
- Ages 20 to 70 yearsHealthy volunteers can take part
- Conditions the study is for
- Gait Disorders
- What is being tested?
- Shoe's sole modification
- How many people
- 50 participants
- Where
- 1 siteTaiwan
- Study dates
- 5 Aug 2024to 30 Aug 2026
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 20 to 70 years
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: Individuals with gait asymmetry or disorder
Who may be able to join
- Self-percieved discomforts in lower extremity
- Be able to walk independently at least for 30 minutes and run independently at least for 10 minutes
Who cannot join
- Obvious foot deformity, trauma in the lower extremity
- Motor or neurologic disorder that affect walking patterns
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Taiwan1 site
China Medical University, Department of Physical Therapy
Taichung, 406040
About this study
Official title: Development of a 2D Video Gait Analysis Platform and Applying to Establish an Artificial Intelligent (AI) Model for the Adjustment of the Customized Shoes' Sole
Artificial intelligence (AI) technology makes gait analysis based on two-dimensional images feasible. The first aim of this study is to develop an analysis platform that uses two-dimensional image analysis technology to determine the relative excursion of body segments and limbs in gait. The second aim is to apply this analysis platform to develop an artificial intelligence model for customized shoe adjustment to optimize gait. For the methodology, OpenPose will be used as the analysis tool to quantify the relative shifts between specific joint/node trajectories of the body, and the VICON three-dimensional motion analysis system will be used to verify the acquisition parameters. For the second aim, an existing 2D gait image database before customized shoe adjustment will be used to obtain the inputs to the customized shoe adjustment AI model. Through training the AI model, enabling to generate the outputs of the most appropriate adjustment parameters for shoe soles. After the AI model has well-trained, the walking kinematics and foot pressure before and after wearing ordinary sports shoes or customized shoes will be compared during running on a treadmill. This research is expected to establish an efficient customized shoe adjustment AI model, allowing users to improve walking efficiency.
Read the detailed description
Walking is a popular form of physical activity and exercise, but long-term walking is also associated with the risk of injury, so gait optimization should be considered. The application of biomechanics to injury prevention must meet practical requirements, including low cost, accessibility, rapid, effectiveness, and less physical effort. Footwear-based interventions have the opportunity to meet these requirements. Various designs of running shoes attempt to reduce injuries and improve performance, and adjustments to sole structure can have a significant impact on gait performance. Artificial intelligence (AI) technology makes gait analysis based on two-dimensional images feasible. The aim of this study for the first year is to develop an analysis platform that uses two-dimensional image analysis technology to determine the relative excursion of body segments and limbs in gait. The aim for the second year is to apply this analysis platform to develop an artificial intelligence model for customized shoe adjustment to optimize gait. Research methods. For the methodology, OpenPose will be used as the analysis tool in the first year to quantify the relative shifts between specific joint/node trajectories of the body, and the VICON three-dimensional motion analysis system will be used to verify the acquisition parameters. In the second year, an existing two-dimensional gait image database before customized shoe adjustment will be used to find the most relevant gait parameters using the aforementioned analysis platform, as the inputs to the customized shoe adjustment AI model. Through training the AI model, enabling to generate the outputs of the most appropriate adjustment parameters for customized shoe soles. After the AI model has well-trained, the walking kinematics and foot pressure before and after wearing ordinary sports shoes or customized shoes will be compared, as well as the differences in striking vibration and kinematics during running on a treadmill. This research is expected to establish an efficient customized shoe adjustment AI model, allowing users to achieve a smoother gait, more even foot pressure distribution, reduce joint loading, improve walking efficiency, and thereby reduce the risk of injury from long-term walking and running.
- Conditions
- Gait Disorders
- Being tested
- Shoe's sole modification
- Keywords
- gait asymmetry
- Medical subject headings
- Mobility Limitation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Shoe's sole modificationOther
Shoe's sole modification by insertion of supporting materials
| Study group | Role | What they receive |
|---|---|---|
| Gait Asymmetry | Individual with gait asymmetry or disorder, which potentially can be modified with shoe's adjustment Receives: Other: Shoe's sole modification |
What is being measured
The primary outcome is the main question the study is designed to answer.
kinemaitc asymmetry
kinemaitc asymmetry during level walking
Measured: Day 1 (After enrollment, a single session for assessment of the gait changes wearing different shoes)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted4 Jun 2025
- Study start5 Aug 2024
- Primary completion31 Jul 2026 (estimated)
- Study completion30 Aug 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for Gait DisordersStudies 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
- Case only
- Time perspective
- Cross-sectional
- Enrolment
- 50 participants (estimated)
Sponsor and investigators
- Lead sponsor
- China Medical University Hospital
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Hsiu-Chen Lin, China Medical University, Taiwan
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07003087
- Sponsor's protocol ID
- CMUH113-REC3-036
- Status last verified
- Feb 2025
- First submitted
- 26 Feb 2025
- Last updated
- 4 Jun 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 4 Jun 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development of a 2D Video Gait Analysis Platform and Applying to Establish an Artificial Intelligent (AI) Model for the Adjustment of the Customized Shoes' Sole
- ClinicalTrials.gov ID
- NCT07003087
- Acronym / sponsor ID
- CMUH113-REC3-036
- Conditions
- Gait Disorders
- Keywords
- gait asymmetry
- Study type
- Observational
- Recruitment status
- Enrolling by invitation
- Enrolment
- 50 participants (estimated)
- Lead sponsor
- China Medical University HospitalUniversity, hospital or other
- Study start
- 5 Aug 2024 (actual)
- Primary completion
- 31 Jul 2026 (estimated)
- Study completion
- 30 Aug 2026 (estimated)
- First posted
- 4 Jun 2025 (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.
Artificial intelligence (AI) technology makes gait analysis based on two-dimensional images feasible. The first aim of this study is to develop an analysis platform that uses two-dimensional image analysis technology to determine the relative excursion of body segments and limbs in gait. The second aim is to apply this analysis platform to develop an artificial intelligence model for customized shoe adjustment to optimize gait. For the methodology, OpenPose will be used as the analysis tool to quantify the relative shifts between specific joint/node trajectories of the body, and the VICON three-dimensional motion analysis system will be used to verify the acquisition parameters. For the second aim, an existing 2D gait image database before customized shoe adjustment will be used to obtain the inputs to the customized shoe adjustment AI model. Through training the AI model, enabling to generate the outputs of the most appropriate adjustment parameters for shoe soles. After the AI model has well-trained, the walking kinematics and foot pressure before and after wearing ordinary sports shoes or customized shoes will be compared during running on a treadmill. This research is expected to establish an efficient customized shoe adjustment AI model, allowing users to improve walking efficiency.
Detailed description
Walking is a popular form of physical activity and exercise, but long-term walking is also associated with the risk of injury, so gait optimization should be considered. The application of biomechanics to injury prevention must meet practical requirements, including low cost, accessibility, rapid, effectiveness, and less physical effort. Footwear-based interventions have the opportunity to meet these requirements. Various designs of running shoes attempt to reduce injuries and improve performance, and adjustments to sole structure can have a significant impact on gait performance. Artificial intelligence (AI) technology makes gait analysis based on two-dimensional images feasible. The aim of this study for the first year is to develop an analysis platform that uses two-dimensional image analysis technology to determine the relative excursion of body segments and limbs in gait. The aim for the second year is to apply this analysis platform to develop an artificial intelligence model for customized shoe adjustment to optimize gait. Research methods. For the methodology, OpenPose will be used as the analysis tool in the first year to quantify the relative shifts between specific joint/node trajectories of the body, and the VICON three-dimensional motion analysis system will be used to verify the acquisition parameters. In the second year, an existing two-dimensional gait image database before customized shoe adjustment will be used to find the most relevant gait parameters using the aforementioned analysis platform, as the inputs to the customized shoe adjustment AI model. Through training the AI model, enabling to generate the outputs of the most appropriate adjustment parameters for customized shoe soles. After the AI model has well-trained, the walking kinematics and foot pressure before and after wearing ordinary sports shoes or customized shoes will be compared, as well as the differences in striking vibration and kinematics during running on a treadmill. This research is expected to establish an efficient customized shoe adjustment AI model, allowing users to achieve a smoother gait, more even foot pressure distribution, reduce joint loading, improve walking efficiency, and thereby reduce the risk of injury from long-term walking and running.
Study design
- Observational model
- Case only
- Time perspective
- Cross-sectional
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Gait Asymmetry | – | Other: Shoe's sole modificationIndividual with gait asymmetry or disorder, which potentially can be modified with shoe's adjustment |
Who may qualify
- Ages
- 20 to 70 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- Individuals with gait asymmetry or disorder
Key inclusion criteria (2 listed)
- Self-percieved discomforts in lower extremity
- Be able to walk independently at least for 30 minutes and run independently at least for 10 minutes
Key exclusion criteria (2 listed)
- Obvious foot deformity, trauma in the lower extremity
- Motor or neurologic disorder that affect walking patterns
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 |
|---|---|---|
| Shoe's sole modificationShoe's sole modification by insertion of supporting materials | Other | Gait Asymmetry |
Primary outcome
kinemaitc asymmetry
Time frame: Day 1 (After enrollment, a single session for assessment of the gait changes wearing different shoes)
kinemaitc asymmetry during level walking
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 4 Jun 2025.
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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.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Gait Disorders, run by China Medical University Hospital at 1 site in Taiwan.
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 2 inclusion and 2 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: enrolling by invitation, as of the sponsor's last update on 4 Jun 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
This study is still under way. Results are not available in the source record yet.
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?
kinemaitc asymmetry (time frame: Day 1 (After enrollment, a single session for assessment of the gait changes wearing different shoes)). 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 4 Jun 2025.