Motor Development AssessmentAMD-AINCT07597356
AI-Based Video Analysis for Motor Development Assessment in Children
An observational study of Motor Development Assessment and Artificial Intelligence-Based Movement Analysis, run by Medipol University at 1 site in Turkey (Türkiye).
Trial summary
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. Read more about this study
- Who is it for?
- Ages 5 to 10 yearsHealthy volunteers can take part
- Conditions the study is for
- Motor Development Assessment, Artificial Intelligence-Based Movement Analysis
- What is being tested?
- Observational Assessment Only
- How many people
- 60 participants
- Where
- 1 siteTurkey (Türkiye)
- Study dates
- 1 Jan 2026to 1 Sep 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 5 to 10 years
- All sexes
- Healthy volunteers accepted
- Child
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.
Who may be able to join
- 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
Who cannot join
- 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
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Turkey (Türkiye)1 site, 1 recruiting
- Recruiting
Istanbul Medipol University
Istanbul, Beykoz, 34820
Abdullah Furkan Cangi, +90 533 622 7898, abdullah.cangi@medipol.edu.tr
About this study
Official title: 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.
Read the 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.
- Being tested
- Observational Assessment Only
- Keywords
- Motor Development, Video Analysis, Artificial Intelligence, Deep Learning
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Observational Assessment OnlyOther
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.
| Study group | Role | What they receive |
|---|---|---|
| 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. Receives: Other: Observational Assessment Only |
What is being measured
The primary outcome is the main question the study is designed to answer.
AI-Based Classification Accuracy of Motor Development
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.
Measured: Baseline assessment (Day 1)
Secondary outcomes (4)
Correlation Between AI Predictions and BOT-2 Scores
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.
Measured: Baseline assessment (Day 1)
Mean Absolute Error of AI-Based Motor Score Prediction
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.
Measured: Baseline assessment (Day 1)
Root Mean Square Error of AI-Based Motor Score Prediction
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.
Measured: Baseline assessment (Day 1)
R-Squared Performance of AI-Based Motor Score Prediction
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.
Measured: Baseline assessment (Day 1)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 May 2026
- Study start1 Jan 2026
- Primary completion1 Aug 2026 (estimated)
- Study completion1 Sep 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Motor Development AssessmentStudies 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-control
- Time perspective
- Prospective
- Enrolment
- 60 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Medipol University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Abdullah Furkan Cangi, Medipol University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07597356
- Sponsor's protocol ID
- AMD-2026-01
- Acronym
- AMD-AI
- Status last verified
- May 2026
- First submitted
- 29 Apr 2026
- Last updated
- 19 May 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Undecided
Publications
Deitz JC, Kartin D, Kopp K. Review of the Bruininks-Oseretsky Test of Motor Proficiency, Second Edition (BOT-2). Phys Occup Ther Pediatr. 2007;27(4):87-102.
PubMed 18032151
Study at a glance
From the registry record, last updated by the sponsor on 19 May 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Development and Validation of an Artificial Intelligence-Based System for Assessing Motor Development in Children Using Video Analysis
- ClinicalTrials.gov ID
- NCT07597356
- Acronym / sponsor ID
- AMD-AI / AMD-2026-01
- Keywords
- Motor Development, Video Analysis, Artificial Intelligence, Deep Learning
- Study type
- Observational
- Recruitment status
- Recruiting
- Enrolment
- 60 participants (estimated)
- Lead sponsor
- Medipol UniversityUniversity, hospital or other
- Study start
- 1 Jan 2026 (actual)
- Primary completion
- 1 Aug 2026 (estimated)
- Study completion
- 1 Sep 2026 (estimated)
- First posted
- 19 May 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.
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.
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 design
- Observational model
- Case-control
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Typical Motor Development | – | Other: Observational Assessment OnlyChildren classified as having typical motor development based on BOT-2 scores. This group represents the control group for comparison with atypical motor development profiles. |
Who may qualify
- Ages
- 5 to 10 years
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Child
- 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.
Key inclusion criteria (5 listed)
- 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
Key exclusion criteria (4 listed)
- 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
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 |
|---|---|---|
| Observational Assessment OnlyThis 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. | Other | Typical Motor Development |
Primary outcome
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.
4 secondary outcomes
Correlation Between AI Predictions and BOT-2 Scores
Time frame: Baseline assessment (Day 1)
Mean Absolute Error of AI-Based Motor Score Prediction
Time frame: Baseline assessment (Day 1)
Root Mean Square Error of AI-Based Motor Score Prediction
Time frame: Baseline assessment (Day 1)
R-Squared Performance of AI-Based Motor Score Prediction
Time frame: Baseline assessment (Day 1)
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 19 May 2026.
- Background: PubMed 18032151Deitz JC, Kartin D, Kopp K. Review of the Bruininks-Oseretsky Test of Motor Proficiency, Second Edition (BOT-2). Phys Occup Ther Pediatr. 2007;27(4):87-102.
- Related InfoLink provided by the sponsor.
- Individual participant dataSharing planned: Undecided
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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.
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 Motor Development Assessment and Artificial Intelligence-Based Movement Analysis, run by Medipol University at 1 site in Turkey (Türkiye).
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 5 inclusion and 4 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: looking for participants now, as of the sponsor's last update on 19 May 2026. 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 Turkey (Türkiye). 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?
Contact Abdullah Furkan Cangi, Msc (+90 553 622 7898, abdullah.cangi@medipol.edu.tr), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
AI-Based Classification Accuracy of Motor Development (time frame: Baseline assessment (Day 1)). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07597356, which also lists 1 linked publication.
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 19 May 2026.