Taking human movements has very good prospects of application in sports, animated projects, medicine and health and other areas. This article aims to study the human motion capture system in sports performances based on the Internet of Things technology and wireless inertial sensors. This article first introduces the theory and characteristics of the Internet of Things and motion capture; next, according to the different characteristics of the sensors in the inertial motion capture system, a two-step Kalman filter is proposed to process the accelerometer and the magnetometer separately and, finally, the structure of this article. The human body motion model is used to analyze the acceleration dynamic error that occurs during the motion. In addition, an inertial motion capture system is constructed to obtain and visualize the structure of each motion node. The experimental results in this paper show that the Kalman filtering algorithm can ensure the accuracy of angle estimation under different motion states and has good fault tolerance to external interference. Among them, the error of the static state is reduced by 23.1%.

In order to more accurately judge the influence the attributes of the public network opinion play on the situation of the public network opinion, this paper puts forward a method of situation analysis of the network public opinion which is based on the variable-weighted Choquet fuzzy integral. Firstly, the Shapely values of the attributes are adjusted in line with the variable weight theory. Then, the fuzzy measure is obtained by optimizing the maximum value of the Marchal entropy. Finally, the Choquet fuzzy integral is adopted to integrate the attributes' evaluating values of the concept, which leads to the judgment of the situation of the public network opinion. According to the results of the experiments, this method is feasible and close to the real public network opinion when the incident happened. It can be used to get the real picture of the public network opinion in different phases which can serve as the input for the follow-on rating of the threat level of the public network opinion.

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