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Related Experiment Video

Updated: May 28, 2026

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

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Automatic Infant Movement Assessment Using Pose-LBP Features and a Cost-Sensitive Subspace kNN Ensemble.

Ali Ari¹, Pelin Atalan Efkere², Ecem Yıldız Çangur²

¹Department of Computer Engineering, Faculty of Technology, Gazi University, Ankara 06654, Turkey.

Bioengineering (Basel, Switzerland)

|May 27, 2026

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This summary is machine-generated.

This study introduces an automated system for analyzing infant movements from videos, significantly improving early detection of neurological conditions like cerebral palsy. The AI framework offers accurate and scalable infant movement classification, reducing reliance on expert analysis.

Area of Science:

Biomedical engineering
Computer vision
Developmental pediatrics

Background:

Infant General Movements (GMs) assessment is crucial for early detection of neurological disorders, including cerebral palsy.
Current GM assessment methods rely heavily on subjective expert interpretation, limiting scalability and consistency.

Purpose of the Study:

To develop an automated and interpretable framework for classifying infant movements using pose-based representations from RGB videos.
To reduce dependency on expert evaluation in infant movement analysis for early clinical screening.

Main Methods:

A pose-driven pipeline extracted 2D skeletal key points from videos using a two-stage tracking strategy.
Joint coordinates were normalized, and videos were segmented into temporal windows for feature extraction using Pose-LBP histograms and motion ratio.

Keywords:

cost-sensitive subspace k-nearest neighbor classifier infant movement analysis pose estimation pose local binary pattern

Related Experiment Videos

Last Updated: May 28, 2026

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Quantified Assessment of Infant's Gross Motor Abilities Using a Multisensor Wearable

Published on: May 17, 2024

Classification was performed using a cost-sensitive subspace k-nearest neighbor ensemble (CSS-kNN-E), evaluated via 10-fold cross-validation.

Main Results:

The automated framework achieved high performance metrics: 99.16% accuracy, 99.19% sensitivity, 99.76% specificity, and 99.23% F1-score.
The model demonstrated robust discrimination across different infant movement classes and resilience to class imbalance.

Conclusions:

The proposed framework offers an accurate, scalable, and automated solution for infant movement analysis.
This technology has significant potential for early clinical screening and decision support, complementing expert assessments.