A Pilot Study: Sleep and Activity Monitoring of Newborn Infants by GRU-Stack-Based Model Using Video Actigraphy and Pulse Rate Variability Features
Nagy, Ádám and Róka, Zita Lilla and Jánoki, Imre Gergely and Siket, Máté and Földesy, Péter and Varga, J and Szabó, M and Zarándy, Ákos (2025) A Pilot Study: Sleep and Activity Monitoring of Newborn Infants by GRU-Stack-Based Model Using Video Actigraphy and Pulse Rate Variability Features. APPLIED SCIENCES-BASEL, 15 (12). ISSN 2076-3417 10.3390/app15126779
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Abstract
We introduce a novel system for automatic assessment of newborn and preterm infant behavior—including activity levels, behavioral states, and sleep–wake cycles—in clinical settings for streamlining care and minimizing healthcare professionals’ workload. While vital signs are routinely monitored, the previously mentioned assessments require labor-intensive direct observation. Research so far has already introduced non- and minimally invasive solutions. However, we developed a system that automatizes the preceding evaluations in a non-contact way using deep learning algorithms. In this work, we provide a Gated Recurrent Unit (GRU)-stack-based solution that works on a dynamic feature set generated by computer vision methods from the cameras’ video feed and patient monitor to classify the activity phases of infants adapted from the NIDCAP (Newborn Individualized Developmental Care Program) scale. We also show how pulse rate variability (PRV) data could improve the performance of the classification. The network was trained and evaluated on our own database of 108 h collected at the Neonatal Intensive Care Unit, Dept. of Neonatology of Pediatrics, Semmelweis University, Budapest, Hungary.
| Item Type: | Article |
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| Subjects: | Q Science > QA Mathematics and Computer Science > QA75 Electronic computers. Computer science / számítástechnika, számítógéptudomány |
| Divisions: | Cellular Sensory and Optical Wave Computing Laboratory |
| SWORD Depositor: | MTMT Injector |
| Depositing User: | MTMT Injector |
| Date Deposited: | 07 Jul 2025 04:49 |
| Last Modified: | 07 Jul 2025 04:49 |
| URI: | https://eprints.sztaki.hu/id/eprint/10924 |
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