# An IoT-Based Computational Framework for Healthcare Monitoring in Mobile Environments
## 2 Related Work
### Findings  
After reviewing the representative proposals in this field, some findings can be identified that justify and summarize our contributions to previous works:  
* The number of wearables, mobile devices and other connected things are increasing significantly. This increases the possibilities of using new types of applications that take advantage of their ubiquitous sensing and communication possibilities.
* The utilization of user portable things for aiding biomedical sensing is growing. Several works propose using the devices for medical monitoring. In addition, the combination of wearables and mobile devices allow designing biomedical sensing systems for self-monitoring in a comfortable way.
* The complexity of advanced applications means they are expensive to run only on wearables and/or acquisition devices. The trend of upgrading the overall performance of complex applications used in distributed environments (such as IoT, mobile apps, etc.) is finding ways of providing additional computing power to mobile devices and 'things'.
* The physical effort monitoring in professional sports practice is a very useful tool to understand athletic performance and how the athlete's body works. The new technology is a key enabler for this purpose.  
Figure 1 shows a very general IoT scheme, which is the approach shown in most of the works reviewed in the state of the art. There are many tasks throughout the IoT process that can be divided more efficiently.  
On the one hand, as far as information input is concerned, IoT establishes a very diverse, distributed, and complex series regarding the great diversity of sensors and other devices/sensing elements that collect data, including social networks through their different APIs. Most of the information is directly sent to the cloud, starting with the previous stages of processing, cleaning, transformation, and normalization, as it has been seen in the works reviewed. However, most of this information can be preprocessed in the available resources on current mobile devices. Likewise, in the last stage of this scheme, analysis, and visualization, again the resources of the mobile devices could play an important role to use their processing capabilities in these tasks.  
The research presented in this paper pursues the same objectives as mentioned above and aims to take advantage of the IoT paradigm for designing medical applications and human biomedical monitoring. Our research effort is directed at developing a computational environment integrating heterogeneous computing resources that allows advanced medical application monitoring to be computed. The target devices are those including in the IoT ecosystem around users: wearable devices for measuring different biomedical parameters of athletes in real-time during sporting activities and mobile devices (such as smartphones, tablets, laptops, etc.) where the medical specialist can monitor the athlete performance.  
The advantages of the proposed framework are based on the flexibility in distributing the application workload. The capabilities of the processing resources are efficiently used. This fact can be deducted from the described case study and the acquired simulation data such as the formalization capability (of the application and the deployed distributed infrastructure), and the flexibility in distributing the application workload.