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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.14400 |
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| _version_ | 1866915047241940992 |
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| author | Holmberg, Daniel Airaksinen, Manu Marchi, Viviana Guzzetta, Andrea Kivi, Anna Haataja, Leena Vanhatalo, Sampsa Roos, Teemu |
| author_facet | Holmberg, Daniel Airaksinen, Manu Marchi, Viviana Guzzetta, Andrea Kivi, Anna Haataja, Leena Vanhatalo, Sampsa Roos, Teemu |
| contents | Reliable methods for the neurodevelopmental assessment of infants are essential for early detection of problems that may need prompt interventions. Spontaneous motor activity, or 'kinetics', is shown to provide a powerful surrogate measure of upcoming neurodevelopment. However, its assessment is by and large qualitative and subjective, focusing on visually identified, age-specific gestures. In this work, we introduce Kinetic Age (KA), a novel data-driven metric that quantifies neurodevelopmental maturity by predicting an infant's age based on their movement patterns. KA offers an interpretable and generalizable proxy for motor development. Our method leverages 3D video recordings of infants, processed with pose estimation to extract spatio-temporal series of anatomical landmarks, which are released as a new openly available dataset. These data are modeled using adaptive graph convolutional networks, able to capture the spatio-temporal dependencies in infant movements. We also show that our data-driven approach achieves improvement over traditional machine learning baselines based on manually engineered features. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_14400 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Learning Developmental Age from 3D Infant Kinetics Using Adaptive Graph Neural Networks Holmberg, Daniel Airaksinen, Manu Marchi, Viviana Guzzetta, Andrea Kivi, Anna Haataja, Leena Vanhatalo, Sampsa Roos, Teemu Computer Vision and Pattern Recognition Machine Learning Image and Video Processing 68T06 I.2; I.4; J.3 Reliable methods for the neurodevelopmental assessment of infants are essential for early detection of problems that may need prompt interventions. Spontaneous motor activity, or 'kinetics', is shown to provide a powerful surrogate measure of upcoming neurodevelopment. However, its assessment is by and large qualitative and subjective, focusing on visually identified, age-specific gestures. In this work, we introduce Kinetic Age (KA), a novel data-driven metric that quantifies neurodevelopmental maturity by predicting an infant's age based on their movement patterns. KA offers an interpretable and generalizable proxy for motor development. Our method leverages 3D video recordings of infants, processed with pose estimation to extract spatio-temporal series of anatomical landmarks, which are released as a new openly available dataset. These data are modeled using adaptive graph convolutional networks, able to capture the spatio-temporal dependencies in infant movements. We also show that our data-driven approach achieves improvement over traditional machine learning baselines based on manually engineered features. |
| title | Learning Developmental Age from 3D Infant Kinetics Using Adaptive Graph Neural Networks |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing 68T06 I.2; I.4; J.3 |
| url | https://arxiv.org/abs/2402.14400 |