Continuous monitoring of neonatal care activities remains challenging because routine caregiving procedures are rarely documented in electronic medical records, despite their potential impact on neonatal physiological stability. This study introduces the NEO TINY System (NTS), an integrated AI-enabled monitoring platform capable of synchronizing bedside video with physiological data collected from NICU medical devices.
The proposed framework combines an Inception-v3 Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) network to automatically recognize neonatal caregiving activities from video sequences while simultaneously evaluating associated changes in heart rate and oxygen saturation. The study analyzed video and physiological recordings collected from 10 neonates across two Level III NICUs, capturing 167 patting events, 64 diaper changes, and 108 tube feeding procedures.
The deep learning system achieved 95% training accuracy and 85% validation accuracy while automatically generating structured documentation of neonatal care activities that are typically absent from electronic medical records. The study also demonstrated statistically significant physiological responses associated with routine caregiving activities, highlighting the potential of AI-assisted monitoring to improve neonatal documentation, quantify stress associated with caregiving, and support future intelligent clinical decision systems in NICUs.