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Related Experiment Video

Updated: Jul 21, 2025

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

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Published on: May 17, 2024

Mobile Device-Based Video Screening for Infant Head Lag: An Exploratory Study.

Hao-Wei Chung^1,2,3, Che-Kuei Chang⁴, Tzu-Hsiu Huang⁵

¹Department of Pediatrics, Kaohsiung Medical University Chung Ho Memorial Hospital, Kaohsiung Medical University, Kaohsiung 807, Taiwan.

Children (Basel, Switzerland)

|July 29, 2023

Summary

This summary is machine-generated.

This study developed an artificial intelligence (AI) model using mobile phone videos to assess infant motor skills, offering a new tool for early detection of developmental delays.

Keywords:

artificial intelligence infant head lag motor development delay remote screen smartphone

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Area of Science:

Pediatric neurology
Artificial intelligence in healthcare
Developmental pediatrics

Background:

Video-based motion analysis is used to identify infant motor development delays.
Limitations of lab-recorded images and training datasets hinder current methods.
This study addresses these limitations by developing an AI model using mobile phone videos.

Purpose of the Study:

To develop an artificial intelligence (AI) model for assessing infant motor skills using mobile phone videos.
To overcome the constraints of traditional lab-based assessments and limited datasets.
To enable accessible and remote monitoring of infant development.

Main Methods:

Utilized 270 videos from 41 high-risk infants recorded by parents on mobile devices.
Employed whole-body pose estimation and 3D transformation with a fuzzy-based approach.
Trained the AI model using whole-body skeleton and key point vectors with domain knowledge, assessing Pull to Sit (PTS) levels.

Main Results:

The key point model achieved 88.062% accuracy for level 0 infant motor skills.
The whole-body skeleton model demonstrated 77.667% accuracy for level 0.
Area Under the ROC curve (AUC) for level 3 reached 96.049% (skeleton) and 94.333% (key points).

Conclusions:

An AI model with minimal environmental restrictions can facilitate family-centered developmental delay screening.
This approach enables remote monitoring for infants requiring early intervention.
The AI model offers a promising solution for accessible infant motor skill assessment.