## 4 Conclusions and Future Work  
In this work, we have designed, implemented and analyzed a monitoring system for physiological parameters. This system is based on a distributed architecture, where physiological data of a person are collected by several sensors; next, a Raspberry Pi joins the information and makes a standardization  
Figure 9: Throughput in bits/s. process; then, these data are sent to the Cloud to be processed. Our Cloud system stores the received data and makes a data fusion process in order to indicate the athlete's fatigue status at every moment. This system has been tested in collaboration with a small group of voluntary tri-athletes. A network simulation has been performed to plan a monitoring network for a bigger group of athletes. Finally, we have found that this system is useful for medium-term monitoring of the sports activities. The technology employed in implementing the system is cost-effective. In this study, we have focused on checking valid physiological parameters for athletes, for patients, the elderly, etc. These physiological parameters can be analyzed in order to assess training and the physical capacity of sportsmen and women. Their follow-up is important during training so as to prevent overload and injury during the sports season. The parameters analyzed allow us to undertake a timed follow-up, which presents no inconvenience to the athletes and allows us to send the data obtained by telematics means. Moreover, the introduction of new parameters is possible using this system and the decision to do so depends on the needs of the final application. The performance of an e-Health board has been tested within this system, monitoring the activity of a small group of tri-athletes. From the obtained results, we can see that the e-Health equipment provides a register/logging of the different physiological constants and other bioparameters of interest to monitor sports activity. The sensors employed present normal functioning within the desired and expected parameters and they are equivalent to the obtained from standard devices. Besides, we have added a data fusion algorithm based on Dempster-Shafer inference to determine the state of fatigue in athletes from the normalized data of the sensors. This algorithm is implemented on the cloud in order to reduce the time processing of each RPi and in case of a team monitoring to perform a data combination at the team level. The fact of having the whole process of merging data in the cloud allows us a greater scalability of the system. The communication layer works properly and allows the interchange of information among the athletes, the trainer and the medical doctor/s (if required). Therefore, the system fulfills the required communication protocols in order to safeguard the data and security system and to avoid intrusions. Having said that and with these results, the functionality of the system is valid, as, besides analyzing the validity of the system in terms of how the network functions, based on throughput and delay, and assessing the overall quantity of erroneous bits in the simulation, it is also valid in terms of how the different sensors function. We believe that when this system is used during the training session of a sportsman/woman, either indoor or outdoor, the data that the trainer or medic receives on blood pressure, cardiac rhythm, oxygen pressure, sweating, temperature, ECG (EMG would have to be undertaken on finishing the training because it cannot be done at the same time as ECG) will allow him or her to obtain information on the physical activity that the athlete is undertaking at that moment, and, therefore, to assess whether the effort load is appropriate or not for that particular athlete. The EMG analysis can also provide him or her with information on muscular activity.