# An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments
## 4 Case Study
### Results and Discussion  
Figure 5 shows some mockup applications and a scheme of the information provided by the proposed system in performing advanced monitoring and analysis applications. The candidate parameters to be analyzed for each player and for each football match are cardiac parameters, number of minutes played during the match, ambient temperature, ambient humidity, altitude and distance covered by the player. All these parameters serve to measure the degree of effort of each player during the football matches and the players' physical exertion. The experiments conducted have been designed for the application described. The design of the test is based on the results of previous research works [88; 93; 103; 104] and on our own estimations. In addition, recent methods that propose a real-time segmentation and feature extraction have been reviewed in order to get an improved implementation for wearable and mobile devices [90; 91; 92; 93]. The validation of these methods is usually produced in term of _Sensitivity_ in detecting anomaly behaviours. However, enhancing the detection accuracy is not the main focus of this work. Rather, we strive to provide a flexible approach for leveraging the capabilities of modern sensing and wearable devices in computing complex applications. Table 4 shows the estimation delay cost for the tasks described above. This performance depends on the input data sizes. In this work, we consider 1 KB/s the size of data acquired from a \"standard 12-lead ECG\" sensor device. The computing platforms are (b\({}_{2}\)) a smartwatch equipped with an ARM A7 at 1.4 GHz processor, a (m\({}_{1}\)) tablet with Qualcomm Snapdragon 820 CPU at 2.15 GHz, and a (m\({}_{2}\)) portable computer with an Intel(R) Core(TM) i7-7660U CPU at 2.50 GHz processor. The performance ratio between these devices for a constant input data size according their specifications is estimated in: 1\(\times\) for smartwatch, 3.93\(\times\) for tablet device and 4.56\(\times\) for the laptop. That is, in average, the m\({}_{1}\) is 3.93 times faster, and the m\({}_{2}\) is 4.56 times faster than the b\({}_{2}\) device, respectively. In addition, the portable computer can be equipped with specialized hardware to enhance the digital signal processing, for example, the computation of wavelet transform of the methods involved in computing the t\({}_{5}\) task. Figure 5: Scheme of the monitoring application. (**a**) Overall environment conditions (**b**) Mockup of ECG parameters of football players. It is assumed that highest possible heart rate is around 220 beats per minute, including under condition of intensive sport activity. Thus, after a pulse is detected, it is physiologically impossible for another beat to occur before 0.27 s. This sets a time constraint of 0.27 s for anomaly detection. This is the time constraint of the application for working in real-time situations. This information along with the data shown in the previous table are the input of the computational framework. The design of the data-flows depicted by Figure 3 is the result of the calibration of the system step of the framework, after simulating the delay cost of the tasks in each device for each application context. Other input information can drive to other suitable configurations for distributing the application. To properly computing in real-time the healthcare application in the different application contexts, the workload is distributed among the available resources around the mobile environment. Unlike other proposals for conducting the ECG analysis offline [72; 73] or inside the wearables [102; 103; 104], or even in the cloud [31], this approach allows leveraging the available infrastructure and perform the healthcare monitoring on the fly for a variable number of users. Table 5 shows the computing cost of the whole application according the configured scenarios depicted by Figure 3 for each application context.