Technical Optimization of Physical Fitness and Mental Health Monitoring System under the Background of Big Data

Abstract

With the development of society and science and the continuous development of computer and network communication technology, the application of computer scene monitoring, real-time data collection, and relevant parts of providing relevant graphs, data and other information is growing rapidly. On the other hand, with the aging of the population and the improvement of living standards, people are paying more and more attention to their own health. In this way, telemedicine technology has also been rapidly developed, and the detection and analysis of human physiological parameters and remote monitoring technology have become the current research direction. However, in order to diagnose and treat patients in a timely and accurate manner, it is necessary to monitor patients continuously for a long time. However, due to long-term and continuous monitoring, the host server will inevitably accumulate large amounts of data. A large amount of data will cause inconvenience to medical staff and diagnostic staff. This requires optimization of the physical and mental health monitoring technology system. Based on the background of big data, this paper studies the technical optimization of the physical and mental health monitoring system. Firstly, the literature research method is used to summarize the working process of the physical and mental health monitoring system, and an algorithm of the detection system is introduced, and finally the algorithm is carried out. The simulation experiment is optimized, and the experimental results show that the classification accuracy of the optimized K-means algorithm is increased by 1% due to the addition of the artificial fish school algorithm to optimize the number of initial clusters and the initial center point of the K-means clustering. From the perspective of the test sample, the larger the test sample involved in the simulation analysis, the higher the classification accuracy of the K-means algorithm. After optimization, the average time consumption of the missing data completion algorithm is reduced by 1-2 seconds. This is due to the fact that the number of endpoints of the artificial fish school algorithm is selected as the number of K blocks of the K-means algorithm, which is used as the initial grouping center of the K-means algorithm, thereby reducing the K-means algorithm to sort the missing data time.