# An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments
## 2 Related Work
### Biomedical Sensing and Health Monitoring  
One of the prime challenges of scientific healthcare applications is the streaming of data collected by many sensors deployed across the body. This current situation leads to novel scenarios where there is an intersection between the Internet of Things (with the quantity and quality of new available wearables), Big Data (the enormous amount of data collected by many heterogeneous sources) and real-time environments due to the specific features of the healthcare applications [4]. This new situation is no longer manageable by traditional methods and new computing paradigms need to be defined. All interconnected sensors create a networked Cyber-Physical System (CPS) [25] that is able to monitor the health of a person [5]. Several strategies have been proposed for specific purposes. For example, the study presented in [26] is a system for long-term monitoring of respiration and pulse. This system consists of four non-contact sensors, used in a laboratory to investigate sensitivity and measuring depth, which are textile-integrated into a shirt. In [27], the authors develop a novel adaptive multi-sensor in order to prevent _venous stasis_ condition. This new approach improves and supplements the current therapeutic options by means of pulse synchronous electro-stimulation of the muscle pump. Recently, different approaches aim to take advantage of the communication capabilities of smart phones in combination with wearable devices to develop versatile monitoring techniques. Some of representative works are described below. The research work developed by [PERSON] et al. [28] shows the use of these devices in the recognition of activities and stages, analysis of group behavior, detection of stressful situations and health monitoring in general. The proposal made by [PERSON] et al. [29] presents the bidirectional communication between smartphone and smartwatch in order to offer new possibilities in the field of diabetes self-management by providing easier ways of monitoring Blood Glucose (BG), insulin injections, physical activity and dietary information directly from the wrist. The ubiquitous capability of connected things is being applied in numerous works for remote monitoring of persons at risk, such as the elderly. [PERSON] et al. [30] present a system consisting of a wearable sensor unit, a smartphone and a website in the senior healthcare field. When the sensor detects a fall, it sends an alert using the smartphone via Bluetooth 4.0 to family members or stakeholders. [PERSON] et al. [31] propose a new method for ECG monitoring based on IoT techniques. ECG data are gathered using a wearable monitoring node and are transmitted directly to the IoT cloud using Wi-Fi. The architecture of the IoT-based ECG monitoring systems is proposed. In this case, all the most complex and computationally-intensive processing is developed in the cloud. There are techniques developed around smart mobile devices and the cloud [32]. These methods collect environmental information and user data from sensors and use a set of applications to store, process, display, and communicate data to the cloud. Cloud server is the key in processing data. Other works use wearable systems for acquiring physiological data of elderly patients and other environmental parameters of interest [33]. The data is also transferred to cloud applications to be monitored by medical staff. In all these novel health systems, wearables are able to obtain patient data and send it to smartphones. Smartphones can perform two different tasks: firstly, they send all the data to the server-side layer that processes, analyzes and stores the data, and also shows the processing results; secondly, they are an alternative to processing and analyzing all the data, and presenting the processing results. Table 1 summarizes the contributions. Moreover, in recent years several approaches over a Mobile Cloud Computing (MCC) environment have appeared in different medical subdomains. The research study developed by [PERSON] et al.