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Stress Monitoring Using Machine Learning, IoT and Wearable Sensors.
Al-Atawi, Abdullah A; Alyahyan, Saleh; Alatawi, Mohammed Naif; Sadad, Tariq; Manzoor, Tareq; Farooq-I-Azam, Muhammad; Khan, Zeashan Hameed.
Afiliación
  • Al-Atawi AA; Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi Arabia.
  • Alyahyan S; Applied College in Dwadmi, Shaqra University, Dawadmi 17464, Saudi Arabia.
  • Alatawi MN; Information Technology Department, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi Arabia.
  • Sadad T; Department of Computer Science, University of Engineering & Technology, Mardan 23200, Pakistan.
  • Manzoor T; Energy Research Centre, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan.
  • Farooq-I-Azam M; Department of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore 54000, Pakistan.
  • Khan ZH; Interdisciplinary Research Center for Intelligent Manufacturing & Robotics (IRC-IMR), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi Arabia.
Sensors (Basel) ; 23(21)2023 Oct 31.
Article en En | MEDLINE | ID: mdl-37960574
The Internet of Things (IoT) has emerged as a fundamental framework for interconnected device communication, representing a relatively new paradigm and the evolution of the Internet into its next phase. Its significance is pronounced in diverse fields, especially healthcare, where it finds applications in scenarios such as medical service tracking. By analyzing patterns in observed parameters, the anticipation of disease types becomes feasible. Stress monitoring with wearable sensors and the Internet of Things (IoT) is a potential application that can enhance wellness and preventative health management. Healthcare professionals have harnessed robust systems incorporating battery-based wearable technology and wireless communication channels to enable cost-effective healthcare monitoring for various medical conditions. Network-connected sensors, whether within living spaces or worn on the body, accumulate data crucial for evaluating patients' health. The integration of machine learning and cutting-edge technology has sparked research interest in addressing stress levels. Psychological stress significantly impacts a person's physiological parameters. Stress can have negative impacts over time, prompting sometimes costly therapies. Acute stress levels can even constitute a life-threatening risk, especially in people who have previously been diagnosed with borderline personality disorder or schizophrenia. To offer a proactive solution within the realm of smart healthcare, this article introduces a novel machine learning-based system termed "Stress-Track". The device is intended to track a person's stress levels by examining their body temperature, sweat, and motion rate during physical activity. The proposed model achieves an impressive accuracy rate of 99.5%, showcasing its potential impact on stress management and healthcare enhancement.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Dispositivos Electrónicos Vestibles / Internet de las Cosas Límite: Humans Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article País de afiliación: Arabia Saudita Pais de publicación: Suiza
Texto completo
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Dispositivos Electrónicos Vestibles / Internet de las Cosas Límite: Humans Idioma: En Revista: Sensors (Basel) Año: 2023 Tipo del documento: Article País de afiliación: Arabia Saudita Pais de publicación: Suiza