Brain’s Stress Observation System Using 2-Channels NIRS Based on Classroom Activity

Sunthad, Kant; Niitsu, Yoshihiro; Inoue, Masahiro; Yokemura, Taketoshi

doi:10.1109/icce.2019.8662117

Cited by 4 publications

(3 citation statements)

References 7 publications

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“…Standard tools in clinical stress evaluation include self-reported questionnaires, exemplified by Cohen's Perceived Stress Scale (PSS) [4] , and self-reported visual scales, such as the Visual Analogue Scale for Stress (VASS) [65] . Biomedical researchers utilize biochemical markers like cortisol and α-amylase to detect stress, often inducing a stress state in subjects through the Trier Social Stress Test (TSST) [66] – [70] .…”

Section: Physiological Indicators Of Stressmentioning

confidence: 99%

Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices

Lazarou,

Exarchos

2024

AIMSN

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<abstract> <p>Stress has emerged as a prominent and multifaceted health concern in contemporary society, manifesting detrimental effects on individuals' physical and mental health and well-being. The ability to accurately predict stress levels in real time holds significant promise for facilitating timely interventions and personalized stress management strategies. The increasing incidence of stress-related physical and mental health issues highlights the importance of thoroughly understanding stress prediction mechanisms. Given that stress is a contributing factor to a wide array of mental and physical health problems, objectively assessing stress is crucial for behavioral and physiological studies. While numerous studies have assessed stress levels in controlled environments, the objective evaluation of stress in everyday settings still needs to be explored, primarily due to contextual factors and limitations in self-report adherence. This short review explored the emerging field of real-time stress prediction, focusing on utilizing physiological data collected by wearable devices. Stress was examined from a comprehensive standpoint, acknowledging its effects on both physical and mental well-being. The review synthesized existing research on the development and application of stress prediction models, underscoring advancements, challenges, and future directions in this rapidly evolving domain. Emphasis was placed on examining and critically evaluating the existing research and literature on stress prediction, physiological data analysis, and wearable devices for stress monitoring. The synthesis of findings aimed to contribute to a better understanding of the potential of wearable technology in objectively assessing and predicting stress levels in real time, thereby informing the design of effective interventions and personalized stress management approaches.</p> </abstract>

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Section: Physiological Indicators Of Stressmentioning

confidence: 99%

Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices

Lazarou,

Exarchos

2024

AIMSN

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“…Sunthad et al [ 76 ] wanted to improve class management using a technique that enabled teachers to monitor and analyze student’s stress levels of the brain. A sensor with two channels of near-infrared spectroscopy (NIRS) was used to monitor the brain activity of four students, who participated in this experiment.…”

Section: Review Of the Papersmentioning

confidence: 99%

A Review of Biophysiological and Biochemical Indicators of Stress for Connected and Preventive Healthcare

et al. 2021

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Stress is a known contributor to several life-threatening medical conditions and a risk factor for triggering acute cardiovascular events, as well as a root cause of several social problems. The burden of stress is increasing globally and, with that, is the interest in developing effective stress-monitoring solutions for preventive and connected health, particularly with the help of wearable sensing technologies. The recent development of miniaturized and flexible biosensors has enabled the development of connected wearable solutions to monitor stress and intervene in time to prevent the progression of stress-induced medical conditions. This paper presents a review of the literature on different physiological and chemical indicators of stress, which are commonly used for quantitative assessment of stress, and the associated sensing technologies.

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“…A non-invasive stress monitoring method is proposed in [16]. A stress monitoring system in students during classes is proposed in [17] for better classroom experience. A stress monitoring system using cortisol as a bio-marker is proposed in [18].…”

Section: A Related Prior Researchmentioning

confidence: 99%

SaYoPillow: A Blockchain-Enabled, Privacy-Assured Framework for Stress Detection, Prediction and Control Considering Sleeping Habits in the IoMT

Rachakonda,

Bapatla,

Mohanty

et al. 2020

Preprint

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Considering today's lifestyle, people just sleep forgetting the benefits it provides to the human body. The reasons for not having a productive sleep could be many. Smart-Yoga Pillow (SaYoPillow) is envisioned as a device that may help in recognizing the importance of a good quality sleep to alleviate stress while establishing a measurable relationship between stress and sleeping habits. A system that analyzes the sleeping habits by continuously monitoring the physiological changes that occur during rapid eye movement (REM) and non-rapid eye movement (NREM) stages of sleep is proposed in the current work. In addition to the physiological parameter changes, factors such as sleep duration, snoring range, eye movement, and limb movements are also monitored. The SaYoPillow system is processed at the edge level with the storage being at the cloud. Not having to compromise the user's privacy, SaYoPillow proposes secure data transmission for both uploading and retrieving, and secure storage and communications as an attempt to reduce malicious attacks on healthcare. A user interface is provided for the user to control data accessibility and visibility. SaYoPillow has 96% accuracy which is close to other existing research works. However, SaYoPillow is the only work with security features as well as only work that considers sleeping habits for stress.

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Brain’s Stress Observation System Using 2-Channels NIRS Based on Classroom Activity

Cited by 4 publications

References 7 publications

Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices

Predicting stress levels using physiological data: Real-time stress prediction models utilizing wearable devices

A Review of Biophysiological and Biochemical Indicators of Stress for Connected and Preventive Healthcare

SaYoPillow: A Blockchain-Enabled, Privacy-Assured Framework for Stress Detection, Prediction and Control Considering Sleeping Habits in the IoMT

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