RT journal article
T1 NRP: A Multi-source, Heterogeneous, Automatic Data Collection System for Infants in Neonatal Intensive Care Units
A1 Pigueiras del Real, Janet
A1 Cervera Gontard, Lionel
A1 Benavente Fernández, Isabel
A1 Lubián López, Simón Pedro
A1 Gallero Rebollo, Enrique
A1 Ruiz Zafra, Ángel
A2 Física de la Materia Condensada
A2 Ingeniería Informática
A2 Materno-Infantil y Radiología
K1 Biomedical monitoring
K1 audio/video
K1 Cameras
K1 data collection system
K1 infants
K1 Pediatrics
K1 pose tracking
K1 Real-time systems
K1 Recording
K1 Streaming media
AB The incidence of premature births continues to rise each year and premature babies require specialized care in neonatal intensive care units (NICUs). The intermittent monitoring of these babies, however, poses challenges for clinicians in terms of visualizing and detecting meaningful diagnostic trends. In order to address this issue, the adoption of continuous, multi-parameter, electronic medical record-keeping methods is promising in terms of leveraging advanced analytics techniques and potentially improving health outcomes. In this article, we present the design and implementation of the Neonates Recording Platform (), a hardware-software tool to be deployed at the bedside in a real NICU environment. The enables data from various sources to be collected, labelled, processed and stored. We conducted tests involving the acquisition of neonates' physiological parameters, synchronized with video recordings, in addition to real-time analysis of body pose, with the capture of up to 33 reference points, and audio files from both the infant and the environment. In , the collected data is organized hierarchically in a portable format and is automatically cleaned and validated, thereby ensuring its usability for healthcare professionals and data scientists. Additionally, enables medical staff to configure trials and add customized text or tagging events. A significant contribution of the platform is the integration of a unique computer vision algorithm called , which extracts physiological information (such as heart rate, breath rate, and blood oxygenation) directly from any monitoring device. The development of involved collaboration with medical staff and data scientists, and evaluation took place at the NICU of the Puerta del Mar University Hospital in Cádiz, Spain. Author
PB IEEE. Institute of Electrical and Electronics Engineers
SN 2168-2194
YR 2023
FD 2023
LK http://hdl.handle.net/10498/31421
UL http://hdl.handle.net/10498/31421
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 08-nov-2026