Sistema baseado em tolerância a falha para o monitoramento de práticas esportivas de alto desempenho

Abstract

Currently, high performance sport has demanded a lot from athletes and, therefore, it is essential to have constant monitoring of training, so the athlete’s performance can be analyzed day after day until competition. The IoT (Internet of Things), through Wearables, for data collection, is the best option for monitoring through the information collected, quickly offering the coach better decision-making based on what is perceived in the training. . More and more data is generated from IoT devices and Wearables in sports, and much of this data is lost or misused. In this context, the fault tolerance concept is the set of techniques used to detect, mask and tolerate system failures, allowing a better management of the collection process from the sensors. In this way, with the system close to 100% functioning, the athlete will have access to performance information, in a detailed and relevant way, from training and competitions. Therefore, the present work aims to propose a system capable of monitoring the performance of athletes, in different environments, managing the collection of data from the sensors, through a system that is tolerant to both hardware and software faults. In particular, the system aims to carry out a survey of which sensors are capable of monitoring, and in this way provide accurate detailing. of the information collected, standardizing the data, according to the modality that will be monitored. During the entire monitoring process, the system will check the availability of the hardware in real time. The scientific contribution of this work arose from the need for more reliable systems, this need arose due to the growing demand for high reliability. The management of monitoring of high performance athletes, in different environments, and with the main objective of offering detailed data, from the training of an athlete, for a more assertive decisionmaking by his coach. The proposed system was evaluated, through a case study, carried out with a prototype applied to training, of athletics. Several sensors were used along the way, and a fault-tolerant system will be responsible for keeping the system as available as possible. The results obtained showed that the computational system, proposed through fault tolerance, was successful in the collections. In particular, the evaluated system was able to obtain data from athletes during training in an acceptable time, even with the occurrence of partial failures.