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Al-Atawi, A.A.; Alyahyan, S.; Alatawi, M.N.; Sadad, T.; Manzoor, T.; Farooq-I-Azam, M.; Khan, Z.H. Stress Monitoring. Encyclopedia. Available online: https://encyclopedia.pub/entry/51348 (accessed on 06 July 2024).
Al-Atawi AA, Alyahyan S, Alatawi MN, Sadad T, Manzoor T, Farooq-I-Azam M, et al. Stress Monitoring. Encyclopedia. Available at: https://encyclopedia.pub/entry/51348. Accessed July 06, 2024.
Al-Atawi, Abdullah A., Saleh Alyahyan, Mohammed Naif Alatawi, Tariq Sadad, Tareq Manzoor, Muhammad Farooq-I-Azam, Zeashan Hameed Khan. "Stress Monitoring" Encyclopedia, https://encyclopedia.pub/entry/51348 (accessed July 06, 2024).
Al-Atawi, A.A., Alyahyan, S., Alatawi, M.N., Sadad, T., Manzoor, T., Farooq-I-Azam, M., & Khan, Z.H. (2023, November 09). Stress Monitoring. In Encyclopedia. https://encyclopedia.pub/entry/51348
Al-Atawi, Abdullah A., et al. "Stress Monitoring." Encyclopedia. Web. 09 November, 2023.
Stress Monitoring
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This entry is adapted from the peer-reviewed paper 10.3390/s23218875

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.

healthcare sensor stress

1. Introduction

Through the implementation of remote patient monitoring, the healthcare landscape has undergone a profound transformation due to the advent of the IoT [1]. The continuous tracking of physiological data made possible by the combination of wearable sensors with IoT technology allows for the early detection of health disorders and preventative measures before they escalate. Stress monitoring with wearable sensors and the Internet of Things (IoT) powered by lithium-ion batteries (LiB) is a potential application that can enhance wellness and preventative health management. A promising application that can improve comfort and preventative health management is stress monitoring with LiB-based IoT and wearable sensors. Stress, a pervasive health concern with potential physical and psychological ramifications, can be effectively addressed through this approach. Stress monitoring, when performed thoroughly and responsibly, is the modern approach in the healthcare industry that may recommend significant evidence to both patients and healthcare providers. It enables people to actively manage their stress and gives healthcare professionals the ability to provide individualized interventions and support. The scope of the IoT extends far beyond healthcare, encompassing domains such as energy efficiency, transportation, agriculture, logistics, and more [1]. However, its impact on healthcare, bolstered by the synergy of IoT and Artificial Intelligence (AI), has been particularly transformative [2]. This convergence has facilitated the transition of medical testing and healthcare services from hospitals to homes, thereby democratizing access to medical equipment for both individuals and healthcare professionals [2]. The amalgamation of mobile sensors with IoT infrastructure enhances data accuracy, and the fusion of an Android app with IoT technology enhances the usability of medical devices. The ripple effect of IoT is poised to be especially profound in the medical sector, promising an elevation in the overall quality of life [2]. In both daily life and healthcare, the applications of IoT and AI are manifold. As the utilization of the Internet has surged exponentially, conventional patient service methodologies have given way to electronic healthcare systems, leading to a decline in traditional modes of communication [3]. In recent times, IoT technology has empowered patients and healthcare practitioners to access cutting-edge medical equipment and resources. The benefits of AI and IoT extend across various realms, including mechanical automation, remote monitoring, convenience, financial efficiency, and enhanced patient satisfaction within healthcare applications [3]. For a sensor to qualify as a constituent of the IoT healthcare system, it must fulfill three core criteria. Firstly, it should be capable of monitoring pulse-related processes such as blood glucose levels, ECG, and oxygen levels. Secondly, it must possess the capacity to detect and gather environmental data like temperature, light, and precipitation. Thirdly, it should be equipped to autonomously transmit data, either dynamically or via an alternative mechanism, to a centralized controller. Following the completion of its designated task, the sensor should transition into an interactive mode, promptly alerting medical professionals for swift action [4]. The versatility of DNA origami extends not only to construction, transportation, and computation on two- and three-dimensional surfaces but also as a significant component of nanotechnology [5][6][7].
Progressive analyses of electronic health records (EHRs) and medical imaging have empowered researchers to enhance healthcare systems through innovative means. While healthcare apps and services are inherently geared toward meeting user needs, the extent of their development hinges on the capabilities and expertise of their developers. Recent explorations have delved into a diverse array of applications employing convolutional neural networks and other machine-learning methodologies. Notably, these techniques have been employed for accurate grading of alcohol dependence, estimation of accident severity, and recognition of emotions through technology [8][9]. The integration of the IoT and artificial intelligence has ushered in significant enhancements in daily life and healthcare. The conjunction and convergence of wearable sensors, IoT, and machine learning (ML) enable healthcare practitioners to diagnose and intervene in patients’ conditions at an early stage, thereby optimizing health outcomes. The multifaceted benefits of IoT devices, encompassing electronic information management, controlled communication, and system processing, have made them a focal point in medical applications due to their convenience, cost-efficiency, and augmented patient satisfaction.
Stress, a pervasive health concern, can be identified and managed effectively through the continuous monitoring of physiological signals via wearable sensors [10]. Recent advancements in IoT and machine learning have been instrumental in enhancing stress monitoring. Notably, wearable sensor systems have been developed to detect and track stress by analyzing physiological parameters like skin conductance and heart rate variability alongside contextual factors such as location and activity levels [11]. The multidisciplinary field of machine learning (ML) heavily relies on visualization, optimization, and theories of probability and decision-making. In contrast to studying each trait or feature separately, machine learning algorithms can handle enormous volumes of data efficiently and allow researchers to find patterns by simultaneously examining a mixture of qualities from the datasets. One of the key characteristics responsible for the enormous success of ML tools is their capacity to recognize a hierarchy of features and infer generalized trends from given data.
The integration of ML algorithms has facilitated the analysis of wearable sensor data, culminating in personalized feedback and interventions for users. The employment of ML and IoT for stress monitoring holds the possibility to enhance efficacy and accuracy. ML methods enable real-time analysis of acquired data, allowing for the early diagnosis and proceeding of stress-related conditions that could otherwise adversely impact overall health and quality of life. The transformative potential of IoT and ML in the healthcare sector is immense, encompassing continuous physiological signal monitoring, personalized interventions, and feedback mechanisms [12].

2. Stress Monitoring

A set of essential characteristics for an ideal sensor includes qualities such as precision, sensitivity, linearity, repetition, reproducibility, drift, calibrating, and fast response [13]. When ML techniques are used, a positive feedback loop may be created, allowing for ongoing improvements in the therapeutic interventions provided to specific patients [14]. Almost any digital device, from wearable to other hardware kinds, can serve as an IoT device and be useful in a wide range of societal areas [15]. A comprehensive review of stress detection research is provided in a scholarly article [16]. Additionally, a study has found that muscle-to-muscle communication is influenced during periods of stress [17]. The specific issue of stress experienced while driving is addressed in another publication [18]. In [19], a stress monitoring approach is proposed that leverages data collected from a smartwatch. Furthermore, ref. [20] introduces a non-invasive method for stress monitoring. Another research paper [21] suggests a stress monitoring system that utilizes cortisol as a biomarker. However, it is important to note that none of these sources discuss the concept of “smart sleep” or include mechanisms for stress control, secure data transfer, or storage. In the context of sleep patterns, ref. [22] presents a study involving participants and utilizes existing wearable devices such as [23][24]. Furthermore, a few non-wearable solutions for sleep regulation are also available [25]. However, it is worth mentioning that these studies do not adequately consider other physiological features and do not emphasize the significance of secure transmission and storage mechanisms for the collected data. According to Bone et al. [26], ML techniques and signal processing can be employed for mental health monitoring continuously. Their work also furnishes a concise assessment of the existing challenges associated with implementing these approaches. While clinical support is accessible, the scarcity of specialized professionals creates difficulties in overseeing all patient activities. Signal processing can be employed to simulate behavior, focusing on the most pertinent situations for making essential decisions.
The actions that trigger the signals picked up by the sensors are referred to as “stressors.” The edge, the fog, or the cloud are all viable locations for computing during the data preparation stage. Edge computing centers are located close to the sensor, and fog computing centers are located between the sensor and the cloud. Cloud computations are those performed using the Internet. To gauge stress levels across various scenarios, researchers have introduced several approaches. These methods involve integrating biofeedback procedures with gaming [27], utilizing mobile phones for tracking [28], and monitoring an individual’s linguistic expressions [29]. Biomarkers used to detect stress levels encompass ECG, skin conductance, respiration, and surface electrocardiography, as outlined in [30]. Other methodologies involve heart rate variability, as in [31], and functional Magnetic Resonance Imaging (fMRI) for stress detection, as detailed in [32]. Numerous use cases exist for a healthcare system in an IoT context, including but not limited to autonomous insulin infusion, sleep monitoring, and mental health monitoring. It is critical to discover solutions for stress detection because global awareness of their significance is at an all-time high.

References

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  2. Mohammed, C.M.; Askar, S. Machine learning for IoT healthcare applications: A review. Int. J. Sci. Bus. 2021, 5, 42–51.
  3. Sadad, T.; Safran, M.; Khan, I.; Alfarhood, S.; Khan, R.; Ashraf, I. Efficient Classification of ECG Images Using a Lightweight CNN with Attention Module and IoT. Sensors 2023, 23, 7697.
  4. Talaat, F.M.; El-Balka, R.M. Stress monitoring using wearable sensors: IoT techniques in medical field. Neural Comput. Appl. 2023, 35, 18571–18584.
  5. Jalali, E.; Soltanizadeh, H.; Chen, Y.; Xie, Y.M.; Sareh, P. Selective hinge removal strategy for architecting hierarchical auxetic metamaterials. Commun. Mater. 2022, 3, 97.
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  7. He, Z.; Shi, K.; Li, J.; Chao, J. Self-assembly of DNA origami for nanofabrication, biosensing, drug delivery, and computational storage. iScience 2023, 26, 106638.
  8. Jayadev, P.G.; Bellary, S. A hybrid approach for classification and identification of iris damaged levels of alcohol drinkers. J. King Saud. Univ-Comput. Inf. Sci. 2022, 34, 5273–5285.
  9. Rao, Z.; Tung, P.Y.; Xie, R.; Wei, Y.; Zhang, H.; Ferrari, A.; Klaver, T.P.; Körmann, F.; Sukumar, P.T.; Kwiatkowski da Silva, A.; et al. Machine learning–enabled high-entropy alloy discovery. Science 2022, 378, 78–85.
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  11. Rachakonda, L.; Mohanty, S.P.; Kougianos, E.; Sundaravadivel, P. Stress-Lysis: A DNN-Integrated Edge Device for Stress Level Detection in the IoMT. IEEE Trans. Consum. Electron. 2019, 65, 474–483.
  12. Khan, A.R.; Saba, T.; Sadad, T.; Nobanee, H.; Bahaj, S.A. Identification of anomalies in mammograms through internet of medical things (IoMT) diagnosis system. Comput. Intell. Neurosci. 2022, 2022, 1100775.
  13. Rosenzweig, C.; Karoly, D.; Vicarelli, M.; Neofotis, P.; Wu, Q.; Casassa, G.; Menzel, A.; Root, T.L.; Estrella, N.; Seguin, B.; et al. Attributing physical and biological impacts to anthropogenic climate change. Nature 2008, 453, 353–357.
  14. Alanazi, H.O.; Abdullah, A.H.; Qureshi, K.N. A critical review for developing accurate and dynamic predictive models using machine learning methods in medicine and health care. J. Med. Syst. 2017, 41, 69.
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  16. Nath, R.K.; Thapliyal, H.; Caban-Holt, A.; Mohanty, S.P. Machine Learning based Solutions for Real-Time Stress Monitoring. IEEE Consum. Electron. Mag. 2020, 9, 34–41.
  17. Lin, S.; Zhang, H.; Gao, Y.; Du, M.; Vai, M. The Effects of Muscle Stress on Signal Transmission in the Intra-Body Communication. In Proceedings of the 2016 IEEE International Conference on Consumer Electronics-China (ICCE-China), Guangzhou, China, 19–21 December 2016; pp. 1–3.
  18. Magaa, V.C.; Muoz-Organero, M. Reducing Stress on Habitual Journeys. In Proceedings of the 2015 IEEE 5th International Conference on Consumer Electronics - Berlin (ICCE-Berlin), Berlin, Germany, 6–9 September 2015; pp. 153–157.
  19. Ciabattoni, L.; Ferracuti, F.; Longhi, S.; Pepa, L.; Romeo, L.; Verdini, F. Real-Time Mental Stress Detection based on Smartwatch. In Proceedings of the 2017 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 8–10 January 2017; pp. 110–111.
  20. Lawanot, W.; Inoue, M.; Yokemura, T.; Mongkolnam, P.; Nukoolkit, C. Daily Stress and Mood Recognition System Using Deep Learning and Fuzzy Clustering for Promoting Better Well-Being. In Proceedings of the 2019 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 11–13 January 2019; pp. 1–6.
  21. Nath, R.K.; Thapliyal, H.; Caban-Holt, A. Validating Physiological Stress Detection Model Using Cortisol as Stress Bio Marker. In Proceedings of the 2020 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA, 4–6 January 2020; pp. 1–5.
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  26. Bone, D.; Lee, C.; Chaspari, T.; Gibson, J.; Narayanan, S. Signal processing and machine learning for mental health research and clinical applications. IEEE Signal Process. Mag. 2017, 34, 196–195.
  27. Osman, H.A.; Dong, H.; Saddik, A.E. Ubiquitous Biofeedback Serious Game for Stress Management. IEEE Access 2016, 4, 1274–1286.
  28. Sano, A.; Picard, R.W. Stress Recognition Using Wearable Sensors and Mobile Phones. In Proceedings of the 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, Geneva, Switzerland, 2–5 September 2013; pp. 671–676.
  29. El-Samahy, E.; Mahfouf, M.; Torres-Salomao, L.A.; Anzurez-Marin, J. A New Computer Control System for Mental Stress Management using Fuzzy Logic. In Proceedings of the 2015 IEEE International Conference on Evolving and Adaptive Intelligent Systems (EAIS), Douai, France, 1–3 December 2015; pp. 1–7.
  30. Wijsman, J.; Grundlehner, B.; Liu, H.; Penders, J.; Hermens, H. Wearable Physiological Sensors Reflect Mental Stress State in Office-Like Situations. In Proceedings of the 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, Geneva, Switzerland, 2–5 September 2013; pp. 600–605.
  31. Choi, J.; Gutierrez-Osuna, R. Using Heart Rate Monitors to Detect Mental Stress. In Proceedings of the 2009 Sixth International Workshop on Wearable and Implantable Body Sensor Networks, Berkeley, CA, USA, 3–5 June 2009; pp. 219–223.
  32. Xu, T.; Cullen, K.R.; Houri, A.; Lim, K.O.; Schulz, S.C.; Parhi, K.K. Classification of Borderline Personality Disorder based on Spectral Power of Resting-State fMRI. In Proceedings of the 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Chicago, IL, USA, 26–30 August 2014; pp. 5036–5039.
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Subjects: Engineering, Biomedical
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Abdullah A. Al-Atawi
,
Saleh Alyahyan
,
Mohammed Naif Alatawi
,
Tariq Sadad
,
Tareq Manzoor
,
Muhammad Farooq-i-Azam
,
Zeashan Hameed Khan
View Times: 190
Revisions: 2 times (View History)
Update Date: 09 Nov 2023
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