AI-Based Video Analysis for Motor Development Assessment in Children (AMD-AI)

May 15, 2026 updated by: Abdullah Furkan Cangi, Medipol University

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

Recruiting

Conditions

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

Observational

Enrollment (Estimated)

60

Contacts and Locations

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Contact

Study Locations

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

Eligibility Criteria

Ages Eligible for Study

  • Child

Accepts Healthy Volunteers

Yes

Sampling Method

Non-Probability Sample

Study Population

The study population consists of children aged 5 to 10 years recruited from schools and clinical settings. All participants are typically developing individuals without prior diagnoses, and they are evaluated to identify variations in motor development patterns using standardized testing and video-based analysis.

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

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

Cohorts and Interventions

Group / Cohort
Intervention / 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.
Other: Observational Assessment Only
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

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

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

This is where you will find people and organizations involved with this study.

Sponsor

Medipol University

Publications and helpful links

The person responsible for entering information about the study voluntarily provides these publications. These may be about anything related to the study.

General Publications

Helpful Links

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

Study Major Dates

Study Start (Actual)

January 1, 2026

Primary Completion (Estimated)

August 1, 2026

Study Completion (Estimated)

September 1, 2026

Study Registration Dates

First Submitted

April 29, 2026

First Submitted That Met QC Criteria

May 15, 2026

First Posted (Actual)

May 19, 2026

Study Record Updates

Last Update Posted (Actual)

May 19, 2026

Last Update Submitted That Met QC Criteria

May 15, 2026

Last Verified

May 1, 2026

More Information

Terms related to this study

Keywords

Other Study ID Numbers

  • AMD-2026-01

Plan for Individual participant data (IPD)

Plan to Share Individual Participant Data (IPD)?

UNDECIDED

Drug and device information, study documents

Studies a U.S. FDA-regulated drug product

No

Studies a U.S. FDA-regulated device product

No

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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