Clinical trial NCT07003087: 2D Video Gait Analysis Platform Applying to AI Model for Adjustment of the Shoes' Sole
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
Study type: OBSERVATIONAL
Overall status: ENROLLING_BY_INVITATION
Conditions: Gait Disorders
Keywords: gait asymmetry
Lead sponsor: China Medical University Hospital
Sponsor class: OTHER
First posted: 2025-06-04
Enrollment: 50
Sex: ALL
Minimum age (years): 20
Maximum age (years): 70
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: yes
Observational model: CASE_ONLY
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: Individuals with gait asymmetry or disorder

Brief 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. 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.

Eligibility criteria: Inclusion Criteria:

* Self-percieved discomforts in lower extremity
* Be able to walk independently at least for 30 minutes and run independently at least for 10 minutes

Exclusion Criteria:

* Obvious foot deformity, trauma in the lower extremity
* Motor or neurologic disorder that affect walking patterns

Primary outcomes:
measure: kinemaitc asymmetry
timeFrame: Day 1 (After enrollment, a single session for assessment of the gait changes wearing different shoes)
Secondary outcomes:
Other outcomes:
Locations:
city: Taichung City
country: Taiwan
facility: China Medical University, Department of Physical Therapy
Study officials:
References: