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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.
Insights
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.
Background:
Pose estimation (PE) has the promise to measure pediatric movement from a video recording. The purpose of this study was to quantify the accuracy of a PE model to detect arm and leg movements in 3-month-old infants with and without (TD, for typical development) complex congenital heart disease (CCHD).
Methods:
Data from 12 3-month-old infants (N = 6 TD and N = 6 CCHD) were used to assess MediaPipe's full-body model. Positive predictive value (PPV) and sensitivity assessed the model's accuracy with behavioral coding.
Results:
Overall, 499 leg and arm movements were identified, and the model had a PPV of 85% and a sensitivity of 94%. The model's PPV in TD was 84% and the sensitivity was 93%. The model's PPV in CCHD was 87% and the sensitivity was 98%. Movements per hour ranged from 399 to 4211 for legs and 236 to 3767 for arms for all participants, similar ranges to the literature on wearables. No group differences were detected.
Conclusions:
There is a strong promise for PE and models to describe infant movements with accessible and affordable resources-like a cell phone and curated video repositories. These models can be used to further improve developmental assessments of limb function, movement, and changes over time.
