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Copyright © 2022 Yang Li et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0/

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.

Details

Title
Real-Time Collection and Analysis of Sports Index Time Series Based on Multimodal Sensor Monitoring
Author
Yang, Li 1   VIAFID ORCID Logo  ; Huang, Ying 1 ; Zhao, Qianqi 1 

 Department of Sports Art, Hebei Institute of Physical Education, Shijiazhuang 050041, China 
Editor
Gengxin Sun
Publication year
2022
Publication date
2022
Publisher
John Wiley & Sons, Inc.
ISSN
1687725X
e-ISSN
16877268
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2640855281
Copyright
Copyright © 2022 Yang Li et al. This is an open access article distributed under the Creative Commons Attribution License (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. https://creativecommons.org/licenses/by/4.0/