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 ( NRP ), a hardware-software tool to be deployed at the bedside in a real NICU environment. The NRP 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 NRP , 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, NRP enables medical staff to configure trials and add customized text or tagging events. A significant contribution of the NRP platform is the integration of a unique computer vision algorithm called CardMed , which extracts physiological information (such as heart rate, breath rate, and blood oxygenation) directly from any monitoring device. The development of NRP 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.

Several challenges arises in any Smart City ecosystem, such as the governance (management) of heterogeneous IoT (Internet of Things) systems deployed and managed by different entities. Usually, IoT-based smart city applications operate in isolation, leading to a fragmented IoT landscape that hinders the managements of the IoT systems in an integrated way. Thus, it is required the involvement of city authorities to structure and organize city-wide interventions among the different IoT systems/applications; but also it is needed the infrastructure and technology that allow these city-wide interventions between the different levels of government (from upper tier authorities to down tier) for the proper management of heterogeneous IoT systems deployed within a Smart City by public as well as private entities. In this paper we propose an infrastructure for the Smart Governance of heterogeneous IoT solutions within Smart Cities, which provides useful features such as the spread of power, security constraints and scalability, among others. The infrastructure is described through 1) a new architectural model, as an adapted version of the basic architectural model, 2) the use of Digital Objects as crucial artifacts to represent, store and interact with physical and digital elements and 3) the use of open-source technology to implements the architectural model and supports the management of the Digital Objects.

Background: Stability during early postnatal life in preterm infants is related to better outcomes. Although vital signs are monitored continuously in Neonatal Intensive Care Unites, this monitoring does not include all physiological parameters nor data such as movement patterns. Although there are scattered sources of data, there is no centralized data hub for neonates information. Results: We have created the first neonate data hub for easy and interactive access to upload or download postural, physiological, and medical data of neonates: NeoVault. NeoVault is a platform that provides access to information through two interfaces: 1) via a Web interface (designed for medical personnel, data scientists, researchers); and 2) via a RESTful API (Application Programming Interfaces) -designed for developers-, aiming to integrate access to information into third-party applications. The web access allows searching and filtering according to specific parameters, visualization of data through graphs and images, and generation of datasets in CSV format. Access through the RESTful API is described in OpenAPI, enabling access to information from any device, facilitating it in an interoperable format. Currently, it contains nearly 800,000 postural records and 3.000 physiological data entries. The physiological and postural data stored for each neonate in NeoVault are collected through the NRP (Neonates Recording Platform) tool, which allows for the automatic and reliable collection of data. Conclusion: NeoVault is an open platform for simple access to postural, physiological, and medical data of neonates that can be utilized by researchers, data scientists, medical personnel, and programmers. It enables integration into third-party applications and the generation of customized datasets.

3D models (or assets) that are present in many of modern software applications are first modeled by graphic designers using dedicated computer graphic tools and then integrated into such software applications or apps by software developers. This simple workflow/procedure requires developers to have a basic grounding in computer graphics, since 3D engines, libraries and third-party software are needed for this kind of integrations. Oftentimes, 3D designers are also required to customize or produce versions of a 3D model and thus, they must re-model all the assets before they are returned back to the developers for integration into the applications. This procedure also occurs whenever a modification or customization is requested. One possible significant improvement to this traditional, poorly automated workflow is to use services-oriented technology and features servitization to carry out the customization of 3D assets on-demand. In this paper, we introduce $\mu$ S3D, an open-source microservices-based platform designed to support features relating to the customization of 3D models. $\mu$ S3D not only enables 3D assets to be customized without the need for computer graphic tools or designers, but also allows 3D models to be visualized through web technologies (e.g. HTML, Javascript and web component to visualize and interact with 3D models), thereby avoiding the development of computer graphics libraries or components in final software products. The paper describes the elements that $\mu$ S3D comprises, explains how it works and presents a series of load tests to compare the performance (time consumption, CPU and memory utilization) of $\mu$ S3D when implemented and deployed as a microservices platform against a monolithic-based implementation, showing similar results with a low number of users (and requests) but reducing, on average, $64.32\%$ the response time in the microservice-based implementation for a large number of users; reducing CPU utilization on microservice-based implementation and remaining the memory usage more or less constant in both implementations.

Background: Traditional neonatal assessments rely on anthropometric measures such as weight, body size, and head circumference. However, recent studies suggest that objective movement quantification may serve as a complementary clinical indicator of development in preterm infants. Methods: This study evaluates non-invasive computer vision-based quantification of neonatal movement using contactless pose tracking based on computer vision. We analyzed approximately 800,000 postural data points from ten preterm infants to identify reliable algorithms, optimal recording duration, and whether whole-body or regional tracking is sufficient. Results: Our findings show that 30 s video segments are adequate for consistent motion quantification. Optical flow methods produced inconsistent results, while distance-based algorithms—particularly Chebyshev and Minkowski—offered greater stability, with coefficients of variation of 5.46% and 6.40% in whole-body analysis. Additionally, Minkowski and Mahalanobis metrics applied to the lower body yielded results similar to full-body tracking, with minimal differences of 0.89% and 1%. Conclusions: The results demonstrate that neonatal movement can be quantified objectively and without physical contact using computer vision techniques and reliable computational methods. This approach may serve as a complementary clinical indicator of neonatal progression, alongside conventional measures such as weight and size, with applications in continuous monitoring and early clinical decision-making for preterm infants.