Research Article
Human Physiological Behavior Understanding and Parameter Tracking Based on Complex Network Theory
@INPROCEEDINGS{10.1007/978-3-030-94551-0_1, author={Han Li and Peng Du}, title={Human Physiological Behavior Understanding and Parameter Tracking Based on Complex Network Theory}, proceedings={Advanced Hybrid Information Processing. 5th EAI International Conference, ADHIP 2021, Virtual Event, October 22-24, 2021, Proceedings, Part I}, proceedings_a={ADHIP}, year={2022}, month={1}, keywords={Complex network Human physiological behavior Parameter tracking Multi-scale feature}, doi={10.1007/978-3-030-94551-0_1} }- Han Li
Peng Du
Year: 2022
Human Physiological Behavior Understanding and Parameter Tracking Based on Complex Network Theory
ADHIP
Springer
DOI: 10.1007/978-3-030-94551-0_1
Abstract
In order to accurately track, analyze and understand human physiological behavior parameters, a method of human physiological behavior understanding and parameter tracking based on complex network theory is established. By defining the complex network theory, the statistical properties of complex networks are studied, and the central index of correlation degree of nodes is combined to analyze the structural characteristics of complex networks. On this basis, the human skeleton modeling conditions are set up, and the final result of parameter tracking behavior recognition is obtained according to the multi-scale feature extraction process. Experimental results show that the parameter tracking method based on complex network theory can accurately record the changes of human physiological behavior compared with traditional multi-scale human physiological behavior analysis method.
