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Advanced artificial intelligence in heart rate and blood pressure monitoring for stress management

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Abstract

The stress factor has been considered as a primary cause that affects a variety of human health conditions nowadays. Medical studies indicate that long-term stress can result in behavioral and cardiovascular problems directly or indirectly. Many individuals still disregard their signs of stress and do not take appropriate steps before severe physiological and emotional issues arise. In several types of research, the physiological signal obtained by heart rate (HR) and blood pressure (BP) monitoring is used for the assessment of mental stress, and this can be suggested to inform an individual about her (his) state of mind due to its non-invasiveness. Therefore this paper proposes an Artificial Intelligence-based fuzzy assisted Petri net (AI-FAS) method for stress assessment on HR and BP monitoring. The normal HR can be determined by the interval between two successive QRS complexes in the ECG waveform. However, acknowledgment of ECG patterns faces problems because of pathological response and noise caused by time-variant physiology. The variance of the heart rate is measured with the analysis of time and frequency. Fuzzy assisted Petri nets in evaluating the stress assessment for HR. BP monitoring for stress management is achieved by the transient time of each pulse. The strength of fuzzy systems indicates interpretability versus the accuracy of two different requirements. Furthermore, the findings indicate that the performance rate of 93.55%, precision 89.01%, recall 89.50%, adaption 89.901% has been numerically validated in stress management.

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Acknowledgements

This work was supported by the Gansu Provincial First-class Discipline Program of Northwest Minzu University (No. 11080305) and the Program for Innovative Research Team of SEAC (No. [2018] 98).

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Authors and Affiliations

  1. School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, 730124, Gansu, China

    Qiang Lin & Tongtong Li

  2. Key Laboratory of Streaming Data Computing Technologies and Application, Northwest Minzu University, Lanzhou, 730124, Gansu, China

    Qiang Lin & Tongtong Li

  3. Key Laboratory of China’s Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, 730030, Gansu, China

    Qiang Lin

  4. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka (UTeM), Durian Tunggal, Malaysia

    P. Mohamed Shakeel

  5. Faculty of Technology, Design and Environment, Visual Artificial Intelligence Lab, Oxford Brookes University, Oxford, UK

    R. Dinesh Jackson Samuel

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  1. Qiang Lin
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  2. Tongtong Li
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  3. P. Mohamed Shakeel
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  4. R. Dinesh Jackson Samuel
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Correspondence to Tongtong Li.

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Lin, Q., Li, T., Shakeel, P.M. et al. Advanced artificial intelligence in heart rate and blood pressure monitoring for stress management. J Ambient Intell Human Comput 12, 3329–3340 (2021). https://doi.org/10.1007/s12652-020-02650-3

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  • DOI: https://doi.org/10.1007/s12652-020-02650-3

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