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.
Studienübersicht
Status
Status
Bedingungen
Bedingungen
Intervention / Behandlung
Intervention / Behandlung
Detaillierte Beschreibung
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.
Studientyp
Studientyp
Einschreibung (Geschätzt)
Einschreibung
Kontakte und Standorte
Studienkontakt
Studienkontakt
- Name: Abdullah Furkan Cangi, Msc
- Telefonnummer: +90 553 622 7898
- E-Mail: abdullah.cangi@medipol.edu.tr
Studienorte
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Beykoz
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Istanbul, Beykoz, Türkei (türkiye), 34820
- Rekrutierung
- Istanbul Medipol University
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Kontakt:
- Abdullah Furkan Cangi
- Telefonnummer: +90 533 622 7898
- E-Mail: abdullah.cangi@medipol.edu.tr
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Teilnahmekriterien
Zulassungskriterien
Zulassungskriterien
Studienberechtigtes Alter
- Kind
Akzeptiert gesunde Freiwillige
Probenahmeverfahren
Studienpopulation
Beschreibung
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
Studienplan
Wie ist die Studie aufgebaut?
Designdetails
Anzahl der Gruppen / Kohorten
Kohorten und Interventionen
Gruppe / KohorteGruppe / Kohorte |
Intervention / BehandlungIntervention / Behandlung |
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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.
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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.
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Was misst die Studie?
Primäre Ergebnismessungen
Primäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
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AI-Based Classification Accuracy of Motor Development
Zeitfenster: Baseline assessment (Day 1)
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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.
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Baseline assessment (Day 1)
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Sekundäre Ergebnismessungen
Sekundäre Ergebnismessungen
Ergebnis Maßnahme |
Maßnahmenbeschreibung |
Zeitfenster |
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Correlation Between AI Predictions and BOT-2 Scores
Zeitfenster: Baseline assessment (Day 1)
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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.
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Baseline assessment (Day 1)
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Mean Absolute Error of AI-Based Motor Score Prediction
Zeitfenster: Baseline assessment (Day 1)
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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.
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Baseline assessment (Day 1)
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Root Mean Square Error of AI-Based Motor Score Prediction
Zeitfenster: Baseline assessment (Day 1)
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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.
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Baseline assessment (Day 1)
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R-Squared Performance of AI-Based Motor Score Prediction
Zeitfenster: Baseline assessment (Day 1)
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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.
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Baseline assessment (Day 1)
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Mitarbeiter und Ermittler
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Sponsor
Publikationen und hilfreiche Links
Allgemeine Veröffentlichungen
Nützliche Links
Studienaufzeichnungsdaten
Haupttermine studieren
Studienbeginn (Tatsächlich)
Studienbeginn
Primärer Abschluss (Geschätzt)
Primärer Abschluss
Studienabschluss (Geschätzt)
Studienabschluss
Studienanmeldedaten
Zuerst eingereicht
Zuerst eingereicht
Zuerst eingereicht, das die QC-Kriterien erfüllt hat
Zuerst eingereicht, das die QC-Kriterien erfüllt hat
Zuerst gepostet (Tatsächlich)
Zuerst gepostet
Studienaufzeichnungsaktualisierungen
Letztes Update gepostet (Tatsächlich)
Letztes Update gepostet
Letztes eingereichtes Update, das die QC-Kriterien erfüllt
Letztes eingereichtes Update, das die QC-Kriterien erfüllt
Zuletzt verifiziert
Zuletzt verifiziert
Mehr Informationen
Begriffe im Zusammenhang mit dieser Studie
Schlüsselwörter
Andere Studien-ID-Nummern
Andere Studien-ID-Nummern
- AMD-2026-01
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