Real-Time Collection and Analysis of Sports Index Time Series Based on Multimodal Sensor Monitoring

Abstract

With the further development of microelectronics technology and sensors, sensors can be widely embedded in mobile phone devices and portable devices. The use of acceleration sensors for human motion monitoring has broad application prospects. Monitoring the daily exercise of the human body is of great significance for formulating scientific exercise and fitness plans and improving physical health. This paper uses the measurement data of multiple types of sensors to propose an index recognition method based on the fusion of multiple types of sensor information. We take the measurement value of a single type of sensor as input and output the index value of the moving part without a strain sensor. The pattern recognition method is used to establish a pattern library, a recognition library, and a measurement library. This article considers noise interference or malfunction of sensor measurements. Aiming at uncertain factors such as the error of the finite element model, a pattern matching method considering the uncertainty is proposed. This article takes aerobics as an example to simulate and analyze the dynamic response of aerobics under wind load. In addition, by simulating the recognition results under different levels of noise interference, the robustness and anti-interference of the pattern matching method are verified.