# An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments
## 4 Case Study
### Case Description  
An increasing number of coaches and individual athletes are becoming more and more convinced of the benefits of such wearable bio-sensors to obtain valuable indicators. The smart systems created around them allow sports performance to be improved, monitoring individual effort, and preventing injuries or even SD. Nowadays, wearing a chest strap during sports is very realistic, especially in top-level sports where athletes need to collect data constantly to compare them with historical registers. In addition, the technological development of sensing technology will allow to create advanced body devices working in an \"unnoticed\" mode without rigid parts (as smart clothes, patch or skin sensors) and where wires were not necessary and able to overcome the interferences from motion artifacts [83; 84; 85]. This proposal consists of monitoring the heart information of each player during a football match. The idea of football game is due to the media transcendence, but this idea can be extended to any other sport where a variable number of players takes place in a mobile environment. For example, running, cycling, etc. The healthcare application consists in monitoring the ECG of each athlete in order to prevent the sudden cardiac death during the match. The process is based on continuously analyzing the information retrieved from the \"standard 12-lead ECG\". This data provides specific information from the different areas of the heart. This information is usually used for diagnosis in electrocardiography issues [56; 86; 87] and it can be read by a chest strap wearable sensor. By means of this monitoring and analysis process, the system can infer the main ECG-based Key Performance Indicators (KPIs) for predicting a heart attack in a player. In the mentioned application scenario, the proposed framework can provide processing distribution by using the methodology depicted in Table 3. First, the overall activity must be properly broken down into tasks and data flows. As a simple example of the functions that take place in ECGs analysis [88; 89; 90; 91; 92; 93; 94; 95; 96], the following tasks are defined according the scheme depicted in Figure 3a. Other tasks and operation may be needed for each of the previous diagnosis methods. Tasks \(\equiv\) T = {t\({}_{1}\): data acquisition; t\({}_{2}\): noise reduction; t\({}_{3}\): filtering; t\({}_{4}\): Segmentation; t\({}_{5}\): Feature extraction; t\({}_{6}\): anomaly detection; t\({}_{7}\): visualization; t\({}_{8}\): Store Data; t\({}_{9}\): Raise alarm}. t\({}_{1}\): _Data acquisition_: The data acquisition is always made by the biosensors. A modern ECG device is basically a digital system. Before any processing, the analog ECG signal is immediately converted into a digital form by an Analog to Digital Conversion (A/D) at a particular sampling rate or frequency for further usage. \(\mathfrak{t}_{2}\): _Noise reduction_: The noise reduction is an essential task for ECG analysis. The bio-signals can be extremely noisy due to the extremely weak signals read. It is expected that this task only removes the noise without changing the desired signal [88]. Thus, this task only cuts off low and high frequency noise. \(\mathfrak{t}_{3}\): _Filtering_: This task is generally designed to attenuate or remove some frequencies from the input data, enhance signal and make the work of the next tasks easier. There exist several types of methods for filtering a signal: linear, nonlinear and polynomial filtering, high-pass filters, finite impulse response filters, etc. [89]. \(\mathfrak{t}_{4}\): _Segmentation_: Heartbeat segmentation allows to analyze the ECG signal. Different methods have been used to improve the detection accuracy of QRS complex [96]. For example, the Hilbert transform allows to detect the R peak and to segment the morphology of QRS complex along the ECG [95].