# An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments
###### Abstract  
The new Internet of Things paradigm allows for small devices with sensing, processing and communication capabilities to be designed, which enable the development of sensors, embedded devices and other 'things' ready to understand the environment. In this paper, a distributed framework based on the internet of things paradigm is proposed for monitoring human biomedical signals in activities involving physical exertion. The main advantages and novelties of the proposed system is the flexibility in computing the health application by using resources from available devices inside the body area network of the user. This proposed framework can be applied to other mobile environments, especially those where intensive data acquisition and high processing needs take place. Finally, we present a case study in order to validate our proposal that consists in monitoring footballers' heart rates during a football match. The real-time data acquired by these devices presents a clear social objective of being able to predict not only situations of sudden death but also possible injuries. Internet of Things; healthcare monitoring; wearable sensing; sensor network; case studies 1  
[PERSON], [PERSON], M.  
information from different parts of the human body have been developed [14]. The devices able to sense human bio-signals are known as biomedical sensors. Their design has been guided by the need to make them lighter, less intrusive for human activity as well as able to provide value-added services to the user. These design criteria have helped to integrate them into the new wearables devices and to popularise them among citizens [15; 16]. Moreover, mobile devices are evolved enough and now, these devices are able to work with biomedical data and run health-related Apps [17; 18]. In this way, recent applications have been developed around the aforementioned concepts where the sensing capabilities of the 'things' play an important role for analysing human behaviour and health [19; 20]. There exist many applications and devices that play an increasingly important role in collecting personal health data. Their ability to acquire the electrocardiogram (ECG) data by means of wearable devices, and combine them with users' mobile devices has enabled the monitoring of heart health metrics for different purposes such as medical care, training and wellness monitoring. However, sensors and other 'things' should have the ability to understand, at least partly, the physical world by themselves [21; 22]. Current trends in cognitive science are still a challenge as they require a huge degree of interdisciplinarity. With changes to models of society and recent technologies used in telemedicine as well as in all healthcare sectors, the traditional methods seem insufficient for this new situation. This new state requires using new techniques to address the problem. Although, traditional methods are still valuable, they need to be used together, providing new architecture models. The focus of the research conducted in this work lies in using these technologies for sensing, and data processing in order to create some intelligence in the devices themselves and collaborate in providing advanced applications to the user. This paper also presents a prototype to demonstrate and validate the proposed solution. The evaluation of the proposal is performed through the assessment of the global performance of the system in delivering computation power. An application example to prevent the health risks associated with physical activity is also described. So, if a medical expert system could detect these causes in real-time during the sports competition by means of analyzing the bio-signals acquired by wearable sensors, medical staff could manage the situation and try to prevent the health risks in sport. As dramatic example of these risks, the sudden arrhythmic death syndrome was the most prevalent cause of Sudden Death (SD) in athletes [23].