An elderly health monitoring system based on biological and behavioral indicators in internet of things
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ORIGINAL RESEARCH
An elderly health monitoring system based on biological and behavioral indicators in internet of things Mehdi Hosseinzadeh1,2 · Jalil Koohpayehzadeh3 · Marwan Yassin Ghafour4 · Aram Mahmood Ahmed5,6 · Parvaneh Asghari7 · Alireza Souri2,8 · Hamid Pourasghari2 · Aziz Rezapour2 Received: 7 August 2019 / Accepted: 27 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
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. Keywords Internet of things · Health monitoring system · Smart elderly care · Data mining
* Alireza Souri [email protected]
2
Health Management and Economics Research Centre, Iran University of Medical Sciences, Tehran, Iran
Mehdi Hosseinzadeh [email protected]
3
Jalil Koohpayehzadeh [email protected]
Department of Community Medicine, Preventive Medicine and Public Health Research Center, Iran University of Medical Sciences, Tehran, Iran
4
Marwan Yassin Ghafour [email protected]
Department of Computer Science, College of Science, University of Halabja, Halabja, Iraq
5
Aram Mahmood Ahmed [email protected]
Department of Information Technology, Sulaimani Polytechnic University, Sulaymaniyah, Iraq
6
International Academic Office, Kurdistan Institution for Strategic Studies and Scientific Research, Sulaymaniyah, Iraq
7
Department of Computer Engineering, Central Tehran Branch, Islamic Azad University, Tehran, Iran
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