AI-Based Video Analysis for Motor Development Assessment in Children (AMD-AI)
Development and Validation of an Artificial Intelligence-Based System for Assessing Motor Development in Children Using Video Analysis
This is a non-interventional, prospective observational study aimed at developing and validating an artificial intelligence-based system for assessing motor development in children using video analysis. Children aged 5 to 10 years will perform standardized motor tasks, which will be recorded under controlled conditions. The recorded videos will be analyzed using computer vision and deep learning techniques to extract movement patterns.
The results of the AI-based analysis will be compared with standardized motor assessment scores obtained from the Bruininks-Oseretsky Test of Motor Proficiency, Second Edition - Short Form (BOT-2 SF). Participants will be classified into typical and atypical motor development groups based on BOT-2 scores. The primary objective is to evaluate the classification performance of the AI model. Secondary analyses will examine the relationship between AI predictions and continuous motor performance scores.
The study is designed to explore whether motor development can be assessed objectively without direct clinical testing, using only short video recordings. The findings may contribute to the development of scalable and accessible digital screening tools for early identification of motor development differences in children.
Study Overview
Status
Status
Conditions
Conditions
Intervention / Treatment
Intervention / Treatment
Detailed Description
This study is a prospective, non-interventional observational study conducted to develop and validate an artificial intelligence-based system for the assessment of motor development in children. The study includes children aged between 5 and 10 years who have no previously diagnosed neurological, developmental, or orthopedic disorders.
All participants will complete the Bruininks-Oseretsky Test of Motor Proficiency, Second Edition - Short Form (BOT-2 SF), which will serve as the reference standard for motor performance. Based on BOT-2 scores, participants will be categorized into typical and atypical motor development groups using predefined thresholds derived from normative data and statistical distribution methods.
In addition to standardized testing, participants will perform a series of structured motor tasks, including jumping jacks, tandem walking, skipping, single-leg balance, finger-to-nose coordination, and protective extension responses. These tasks will be recorded using high-resolution video under controlled environmental conditions.
Video data will be processed using computer vision pipelines. Skeletal keypoints will be extracted using pose estimation models, and silhouette segmentation will be obtained using deep learning-based segmentation models. Extracted features will be normalized and used as input for machine learning and deep learning architectures, including transformer-based models and graph-based networks.
The primary outcome is the classification performance of the AI model in distinguishing typical versus atypical motor development profiles, evaluated using metrics such as ROC-AUC, accuracy, sensitivity, specificity, F1-score, and balanced accuracy. Secondary outcomes include regression performance for predicting continuous motor scores, evaluated using MAE, RMSE, and R-squared values.
Inter-rater reliability of expert evaluations will be assessed using intraclass correlation coefficients (ICC). Additional analyses will include error distribution examination and Bland-Altman analysis to assess agreement between AI predictions and standardized test scores.
This study does not involve any intervention, treatment, or risk beyond standard observational procedures. All participants are healthy volunteers, and informed consent will be obtained from parents or legal guardians. The study has been approved by the Istanbul Medipol University Non-Interventional Clinical Research Ethics Committee.
Study Type
Study Type
Enrollment (Estimated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Abdullah Furkan Cangi, Msc
- Phone Number: +90 553 622 7898
- Email: abdullah.cangi@medipol.edu.tr
Study Locations
-
-
Beykoz
-
Istanbul, Beykoz, Turkey (Türkiye), 34820
- Recruiting
- Istanbul Medipol University
-
Contact:
- Abdullah Furkan Cangi
- Phone Number: +90 533 622 7898
- Email: abdullah.cangi@medipol.edu.tr
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
- Child
Accepts Healthy Volunteers
Sampling Method
Study Population
Description
Inclusion Criteria:
- Children aged between 5 and 10 years
- No diagnosed neurological, developmental, or orthopedic disorders
- Ability to follow verbal instructions
- Informed consent obtained from parents or legal guardians
- No prior participation in sensory integration therapy or special education programs
Exclusion Criteria:
- Diagnosed neurological, developmental, or orthopedic conditions (e.g., autism spectrum disorder, cerebral palsy, epilepsy)
- Visual or hearing impairments affecting task performance
- Severe attention or behavioral problems preventing test completion
- Physical limitations preventing participation in motor tasks
Study Plan
How is the study designed?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
Intervention / TreatmentIntervention / Treatment |
|---|---|
|
Typical Motor Development
Children classified as having typical motor development based on BOT-2 scores.
This group represents the control group for comparison with atypical motor development profiles.
|
This study does not include any therapeutic or experimental intervention.
The procedures are limited to observational assessment and data collection.
Participants perform standardized motor tasks and are video recorded under controlled conditions.
No treatment, training, or behavioral modification is applied.
The collected data are analyzed using artificial intelligence-based methods to evaluate motor development patterns.
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
AI-Based Classification Accuracy of Motor Development
Time Frame: Baseline assessment (Day 1)
|
Classification accuracy of the artificial intelligence model in distinguishing typical versus atypical motor development based on video analysis, using the Bruininks-Oseretsky Test of Motor Proficiency, Second Edition Short Form (BOT-2 SF) total score as the reference standard.
BOT-2 SF scores range from 0 to 88, with higher scores indicating better motor proficiency.
|
Baseline assessment (Day 1)
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Correlation Between AI Predictions and BOT-2 Scores
Time Frame: Baseline assessment (Day 1)
|
Statistical relationship between artificial intelligence-generated motor development predictions and Bruininks-Oseretsky Test of Motor Proficiency, Second Edition Short Form (BOT-2 SF) total scores.
BOT-2 SF scores range from 0 to 88, with higher scores indicating better motor proficiency.
|
Baseline assessment (Day 1)
|
|
Mean Absolute Error of AI-Based Motor Score Prediction
Time Frame: Baseline assessment (Day 1)
|
Mean absolute error (MAE) of the artificial intelligence model in predicting continuous motor development scores based on video analysis, compared with Bruininks-Oseretsky Test of Motor Proficiency, Second Edition Short Form (BOT-2 SF) total scores.
|
Baseline assessment (Day 1)
|
|
Root Mean Square Error of AI-Based Motor Score Prediction
Time Frame: Baseline assessment (Day 1)
|
Root mean square error (RMSE) of the artificial intelligence model in predicting continuous motor development scores based on video analysis, compared with Bruininks-Oseretsky Test of Motor Proficiency, Second Edition Short Form (BOT-2 SF) total scores.
|
Baseline assessment (Day 1)
|
|
R-Squared Performance of AI-Based Motor Score Prediction
Time Frame: Baseline assessment (Day 1)
|
Coefficient of determination (R-squared) for the artificial intelligence model in predicting continuous motor development scores based on video analysis, compared with Bruininks-Oseretsky Test of Motor Proficiency, Second Edition Short Form (BOT-2 SF) total scores.
|
Baseline assessment (Day 1)
|
Collaborators and Investigators
Sponsor
Sponsor
Publications and helpful links
General Publications
Helpful Links
Study record dates
Study Major Dates
Study Start (Actual)
Study Start
Primary Completion (Estimated)
Primary Completion
Study Completion (Estimated)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (Actual)
First Posted
Study Record Updates
Last Update Posted (Actual)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
Other Study ID Numbers
- AMD-2026-01
Plan for Individual participant data (IPD)
Plan to Share Individual Participant Data (IPD)?
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
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