Research on a personalized classifier of health status based on pulse signal

Xiaoping Jiang; Leli Sun; Shuyao Feng; Zhuojing Li; Ying Chen; Xingzhuo Chen; Chunman Wang; Aolai He

doi:10.1117/12.2691708

21 August 2023 Research on a personalized classifier of health status based on pulse signal

Xiaoping Jiang, Leli Sun, Shuyao Feng, Zhuojing Li, Ying Chen, Xingzhuo Chen, Chunman Wang, Aolai He

Proceedings Volume 12783, International Conference on Images, Signals, and Computing (ICISC 2023); 127830F (2023) https://doi.org/10.1117/12.2691708
Event: International Conference on Images, Signals, and Computing (ICISC2023), 2023, Chengdu, China

Abstract

At present, the workload of mental workers in society is getting heavier and heavier, and it is necessary to assess their health status. Compared with other physiological signals, the pulse is easy to obtain and non-invasive. In this paper, through pulse signal detection, pulse data preprocessing and feature extraction, 12 sets of feature values are selected. Then based on these feature data, using support vector machine algorithm modeling, for different testers to build different personalized human physiological state discrimination system. The experimental results show that the classification accuracy rate reaches 91.17%, which proves that the selected feature value has a strong correlation with the physiological state, and the classifier is effective.

Citation Download Citation

Xiaoping Jiang, Leli Sun, Shuyao Feng, Zhuojing Li, Ying Chen, Xingzhuo Chen, Chunman Wang, and Aolai He "Research on a personalized classifier of health status based on pulse signal", Proc. SPIE 12783, International Conference on Images, Signals, and Computing (ICISC 2023), 127830F (21 August 2023); https://doi.org/10.1117/12.2691708

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