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. TheNRPenables 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. InNRP, 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,NRPenables medical staff to configure trials and add customized text or tagging events. A significant contribution of theNRPplatform is the integration of a unique computer vision algorithm calledCardMed, which extracts physiological information (such as heart rate, breath rate, and blood oxygenation) directly from any monitoring device. The development ofNRPinvolved collaboration with medical staff and data scientists, and evaluation took place at the NICU of the Puerta del Mar University Hospital in Cádiz, Spain.

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 article, we introduce$ \mu $S3D, an open-source microservices-based platform designed to support features relating to the customization of 3D models.$ \mu $S3D not only enables 3D assets to be customized without the need for computer graphic tools or designers, but also allows 3D models to be visualized through web technologies (e.g., HTML, Javascript and web component to visualize and interact with 3D models), thereby avoiding the development of computer graphics libraries or components in final software products. The article describes the elements that$ \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.

In this work we introduce NeoCam, an open source hardware-software platform for video-based monitoring of preterms infants in Neonatal Intensive Care Units (NICUs). NeoCam includes an edge computing device that performs video acquisition and processing in real-time. Compared to other proposed solutions, it has the advantage of handling data more efficiently by performing most of the processing on the device, including proper anonymisation for better compliance with privacy regulations. In addition, it allows to perform various video analysis tasks of clinical interest in parallel at speeds of between 20 and 30 frames-per-second. We introduce algorithms to measure without contact the breathing rate, motor activity, body pose and emotional status of the infants. For breathing rate, our system shows good agreement with existing methods provided there is sufficient light and proper imaging conditions. Models for motor activity and stress detection are new to the best of our knowledge. NeoCam has been tested on preterms in the NICU of the University Hospital Puerta del Mar (Cádiz, Spain), and we report the lessons learned from this trial.

Internet of Things (IoT) is a set of massively emerging technologies that aim to make our lives better. One of the most suitable application domains is healthcare and wellbeing, where IoT technologies can enhance living environments (ELE). IoT applications are deployed into an IoT ecosystem, which is supported by a specific architecture. The usual IoT architecture model has three different layers (Acquisition, Network and Application), where all the different IoT elements are organized. This architecture has been designed for general purposes, but the deployment of IoT healthcare applications supported by this architecture model exhibits important drawbacks. In particular, this architecture model does not consider IoT devices coexisting with wearables devices, which hinders the design of smart habitats. In this paper we present an IoT-aware architectural model for smart habitats with special emphasis on healthcare domain. The new architecture adds a fourth layer to the traditional IoT architecture, describing the software artefacts and tips needed to support context-aware habitats through the interaction between wearable computing devices and IoT devices. The main goal of the IoT architecture presented is to enhance living environments for elderly people.

Patients with chronic diseases or people with special health care needs are typically monitored by various health experts that address the problem from several perspectives. These experts usually do not interact directly between them; therefore, the instructions given to the patient by one of them are provided disregarding advices and instructions provided by the others. The collaboration between different health experts in a real context is mandatory to ensure the proper monitoring of the patient. This kind of collaboration, supported by technology, benefits the users' health condition and helps the patient achieve their goals in terms of wellbeing. This paper presents an approach for collaborative management of events related to activity supervisions and monitoring of these patients types. It also introduces a model and a mobile application to support this collaborative work.