Sports Parameter Acquisition Based on Internet of Things and Wavelet Analysis

Abstract

In this study, a sports parameter acquisition model based on the internet of things and wavelet analysis is studied to improve the accuracy and timeliness of human sports parameter acquisition. A motion parameter acquisition model including a sensing layer, transmission layer, and application layer is designed. The acceleration sensor and temperature sensor in the information acquisition node in the sensing layer are used to collect the motion parameter data, which are uploaded to the application layer by the network in the transmission layer. The received data are denoised by the wavelet analysis method through the data processing unit in this layer and then sent to the ZigBee coordinator for coordination. The results show that the model can achieve the effective acquisition of different sports parameters of different moving objects and analyze the actual movement of moving objects according to the acquisition results. In the acquisition process, the signal burr can be effectively removed, the signal noise can be reduced, the high signal-to-noise ratio signal can be output, and the accuracy of acquisition is improved. It has high timeliness, stable performance, and strong practical application, which can provide an effective guarantee for users to monitor sports parameter data in real time.