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Quantifying Arm and Leg Movements in 3-Month-Old Infants Using Pose Estimation: Proof of Concept
Marcelo R Rosales1, Janet Simsic2, Tondi Kneeland3
1School of Health and Rehabilitation Sciences, The Ohio State University, Columbus, OH 43210, USA.
Sensors (Basel, Switzerland)
|December 17, 2024
Summary
Pose estimation accurately detects infant limb movements, showing promise for developmental assessments in infants with and without complex congenital heart disease. This technology offers accessible tools for tracking pediatric motor development.
Area of Science:
- Biomedical engineering
- Developmental pediatrics
- Computer vision
Background:
- Pose estimation (PE) offers a promising method for quantifying pediatric movement from video recordings.
- Assessing infant movement is crucial for understanding development, especially in those with complex congenital heart disease (CCHD).
Purpose of the Study:
- To quantify the accuracy of a PE model in detecting arm and leg movements in 3-month-old infants.
- To compare PE model accuracy between typically developing (TD) infants and those with CCHD.
Main Methods:
- Utilized MediaPipe's full-body pose estimation model on video data from 12 infants (6 TD, 6 CCHD).
- Assessed model accuracy using behavioral coding, calculating positive predictive value (PPV) and sensitivity for detected limb movements.
Main Results:
- The PE model identified 499 leg and arm movements with an overall PPV of 85% and sensitivity of 94%.
- Accuracy was high in both groups: TD infants (PPV 84%, sensitivity 93%) and CCHD infants (PPV 87%, sensitivity 98%).
- Movement frequency ranges were consistent with existing literature, and no significant group differences were found.
Conclusions:
- Pose estimation models show significant potential for describing infant movements using accessible technology like smartphones.
- This approach can enhance developmental assessments of limb function and track changes over time.
- PE provides a cost-effective and scalable method for pediatric movement analysis.
