The metabolic equivalent of task (MET) is currently the most used indicator for measuring the energy expenditure (EE) of a physical activity (PA) and has become an important measure for determining and supervising a person’s state of health. The use of new devices which are capable of measuring inertial movements by means of built-in accelerometers enable the PA to be measured objectively on the basis of the reckoning of “counts”. These devices are also known as inertial measurement units (IMUs) and each count is an aggregated value indicating the intensity of a movement and can be used in conjunction with other parameters to determine the MET rate of a particular physical activity and thus it’s associated EE. Various types of inertial devices currently exist that enable count calculus and physical activity to be monitored. The advent of mobile devices, such as smartphones, with empowered computation capabilities and integrated inertial sensors, has enabled EE to be measure in a distributed, ubiquitous and natural way, thereby overcoming the reluctance of users and practitioners associated with in-lab studies. From the point of view of the process analysis and infrastructure needed to manage data from inertial devices, there are also various differences in count computing: extra devices are required, out-of-device processing, etc. This paper presents a study to discover whether the estimation of energy expenditure is dependent on the accelerometer of the device used in measurements and to discover the suitability of each device for performing certain physical activities. In order to achieve this objective, we have conducted several experiments with different subjects on the basis of the performance of various daily activities with different smartphones and IMUs.

Good health is the result of a healthy lifestyle, where caring about physical activity and nutrition are key concerns. However, in today’s society, nutritional disorders are becoming increasingly frequent, affecting children, adults, and elderly people, mainly due to limited nutrition knowledge and the lack of a healthy lifestyle. A commonly adopted therapy to these imbalances is to monitor physical activity and daily habits, such as recording exercise or creating custom meal plans to count the amount of macronutrients and micronutrients acquired in each meal. Nowadays, many health tracking applications (HTA) have been developed that, for instance, record energy intake as well as users’ physiological parameters, or measure the physical activity during the day. However, most existing HTA do not have a uniform architectural design on top of which to build other applications and services. In this manuscript, we present system architecture intended to serve as a reference architecture for building HTA solutions. In order to validate the proposed architecture, we performed a preliminary evaluation with 15 well recognized experts in systems and software architecture from different entities around world and who have estimated that our proposal can generate architecture for HTA that is adequate, reliable, secure, modifiable, portable, functional, and with high conceptual integrity. In order to show the applicability of the architecture in different HTA, we developed two telemonitoring systems based on it, targeted to different tasks: nutritional coaching (Food4Living) and physical exercise coaching (TrainME). The purpose was to illustrate the kind of end-user monitoring applications that could be developed.

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.

This paper proposes a model-driven approach for developing high-level software interfaces that allow developers to interact with wearable devices easily. These components hide the heterogeneity of the devices interfaces and provide developers with a simple and homogeneous way to interoperate with these digital peripherals. The use of this approach also allows reducing risks and development efforts.

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.