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An elderly health monitoring system based on biological and behavioral indicators in internet of things

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Abstract

Advancement of sensor technologies has conducted to the rapid evolution of platforms, tools and approaches such as Internet of Things (IoT) for developing behavioral and physiological monitoring systems. Nowadays, According to growing number of elderlies living alone without their relatives scattered over the wide geographical areas, it is significantly essential to track their health function status continuously. In this paper, an IoT-based health monitoring system is proposed to check vital signs and detect biological and behavioral changes via smart elderly care technologies. It provides a health monitoring system for the involved medical teams to continuously monitor and assess a disabled or elderly’s behavioral activity as well as the biological parameters, applying sensor technology through the IoT devices. In this approach, vital data is collected via IoT monitoring objects and then, data analysis is carried out through different machine learning methods such as Decision Tree (J48), Sequential Minimal Optimization (SMO), Multi-Layer Perceptron (MLP) and Naïve Bayes (NB) classifiers for detecting the level of probable risks of elderly’s physiological and behavioral changes. The experimental results confirm that the SMO, MLP and NB classifiers meet approximately close performance considering the accuracy, precision, recall, and f-score factors. However, the J48 method shows the highest performance for health function status predicting in our scenario with 99%, of accuracy and precision, 100% of recall and 97% of f-score. Moreover, the J48 performs with the lowest execution time in comparison to the other applied classifiers.

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Acknowledgement

This paper derives from the Research Project with code 98-2-37-15607 and Approval ID IR.IUMS.REC.1398.798.

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Authors and Affiliations

  1. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Mehdi Hosseinzadeh

  2. Health Management and Economics Research Centre, Iran University of Medical Sciences, Tehran, Iran

    Mehdi Hosseinzadeh, Alireza Souri, Hamid Pourasghari & Aziz Rezapour

  3. Department of Community Medicine, Preventive Medicine and Public Health Research Center, Iran University of Medical Sciences, Tehran, Iran

    Jalil Koohpayehzadeh

  4. Department of Computer Science, College of Science, University of Halabja, Halabja, Iraq

    Marwan Yassin Ghafour

  5. Department of Information Technology, Sulaimani Polytechnic University, Sulaymaniyah, Iraq

    Aram Mahmood Ahmed

  6. International Academic Office, Kurdistan Institution for Strategic Studies and Scientific Research, Sulaymaniyah, Iraq

    Aram Mahmood Ahmed

  7. Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran

    Parvaneh Asghari

  8. Department of Computer Engineering, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran

    Alireza Souri

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  1. Mehdi Hosseinzadeh
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  2. Jalil Koohpayehzadeh
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  3. Marwan Yassin Ghafour
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  4. Aram Mahmood Ahmed
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  5. Parvaneh Asghari
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  6. Alireza Souri
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  8. Aziz Rezapour
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Correspondence to Alireza Souri.

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Hosseinzadeh, M., Koohpayehzadeh, J., Ghafour, M.Y. et al. An elderly health monitoring system based on biological and behavioral indicators in internet of things. J Ambient Intell Human Comput 14, 5085–5095 (2023). https://doi.org/10.1007/s12652-020-02579-7

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  • DOI: https://doi.org/10.1007/s12652-020-02579-7

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