Stress Monitoring Using Machine Learning, IoT and Wearable Sensors
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 applicati...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-10-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/23/21/8875 |
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| author | Abdullah A. Al-Atawi Saleh Alyahyan Mohammed Naif Alatawi Tariq Sadad Tareq Manzoor Muhammad Farooq-i-Azam Zeashan Hameed Khan |
| author_facet | Abdullah A. Al-Atawi Saleh Alyahyan Mohammed Naif Alatawi Tariq Sadad Tareq Manzoor Muhammad Farooq-i-Azam Zeashan Hameed Khan |
| author_sort | Abdullah A. Al-Atawi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-11T11:21:08Z |
| format | Article |
| id | doaj.art-282a61bfcbbf41a39f7db5d420811066 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-11T11:21:08Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-282a61bfcbbf41a39f7db5d4208110662023-11-10T15:12:30ZengMDPI AGSensors1424-82202023-10-012321887510.3390/s23218875Stress Monitoring Using Machine Learning, IoT and Wearable SensorsAbdullah A. Al-Atawi0Saleh Alyahyan1Mohammed Naif Alatawi2Tariq Sadad3Tareq Manzoor4Muhammad Farooq-i-Azam5Zeashan Hameed Khan6Department of Computer Science, Applied College, University of Tabuk, Tabuk 47512, Saudi ArabiaApplied College in Dwadmi, Shaqra University, Dawadmi 17464, Saudi ArabiaInformation Technology Department, Faculty of Computers and Information Technology, University of Tabuk, Tabuk 47512, Saudi ArabiaDepartment of Computer Science, University of Engineering & Technology, Mardan 23200, PakistanEnergy Research Centre, COMSATS University Islamabad, Lahore Campus, Lahore 54000, PakistanDepartment of Electrical and Computer Engineering, COMSATS University Islamabad, Lahore Campus, Lahore 54000, PakistanInterdisciplinary Research Center for Intelligent Manufacturing & Robotics (IRC-IMR), King Fahd University of Petroleum & Minerals (KFUPM), Dhahran 31261, Saudi ArabiaThe 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.https://www.mdpi.com/1424-8220/23/21/8875healthcareIoTmachine learningsensorstress |
| spellingShingle | Abdullah A. Al-Atawi Saleh Alyahyan Mohammed Naif Alatawi Tariq Sadad Tareq Manzoor Muhammad Farooq-i-Azam Zeashan Hameed Khan Stress Monitoring Using Machine Learning, IoT and Wearable Sensors Sensors healthcare IoT machine learning sensor stress |
| title | Stress Monitoring Using Machine Learning, IoT and Wearable Sensors |
| title_full | Stress Monitoring Using Machine Learning, IoT and Wearable Sensors |
| title_fullStr | Stress Monitoring Using Machine Learning, IoT and Wearable Sensors |
| title_full_unstemmed | Stress Monitoring Using Machine Learning, IoT and Wearable Sensors |
| title_short | Stress Monitoring Using Machine Learning, IoT and Wearable Sensors |
| title_sort | stress monitoring using machine learning iot and wearable sensors |
| topic | healthcare IoT machine learning sensor stress |
| url | https://www.mdpi.com/1424-8220/23/21/8875 |
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