Stress and office-syndrome detection using EEG, HRV and hand movement

Reanaree, Parkbhum; Tananchana, Polachet; Narongwongwathana, Worapong; Pintavirooj, C.

doi:10.1109/bmeicon.2016.7859624

Cited by 8 publications

(6 citation statements)

References 6 publications

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“…HRV could be considered a biometric parameter upon which to determine people's stress conditions. Thanks to the use of a tool able to detect heart signals, HRV could be easily collected through wearable devices (Acerbi et al, 2017;Debard et al, 2020;Rani et al, 2002) or other monitoring tools with specific sensors (Mozos et al, 2017;Reanaree et al, 2016;Sriramprakash et al, 2017b;Zalabarria et al, 2017) (Mozos et al, 2017) or a traditional ECG. HRV could be also combined with social media microblogs (Feng et al, 2021).…”

Section: Identification Of the Main Stress Detection Parameters For S...mentioning

confidence: 99%

“…HRV could be also combined with social media microblogs (Feng et al, 2021). 2020; Elzeiny & Qaraqe, 2018;Kalas & Momin, 2016;Reanaree et al, 2016) and helmet (Kalimeri & Saitis, 2016). Although from a medical point of view it could be a non-invasive method thanks to the use of scalp surface, from a perspective of stress monitoring, on the contrary, it is quite intrusive since it requires the use of electrodes.…”

Section: Identification Of the Main Stress Detection Parameters For S...mentioning

confidence: 99%

“…(11) Others Moreover, other studies focus on hand movements (Reanaree et al, 2016), tweeting content (Zhao et al, 2016) keyboard typing (Sağbaş et al, 2020;Vizer et al, 2009) and audio detection (Abburi et al, 2016).…”

Section: (10)mentioning

confidence: 99%

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Application of Smart Technologies for Assessing Users’ Well-Being for Immersive Design Strategies: A State-of-the-Art Review

D'Ascenzi,

Getuli,

Fiesoli

2023

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

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As never before, during the COVID-19 pandemic, the effectiveness of the digital design strategies on the user’s well-being has been questioned. However, a research branch astride digital design and neuroscience able to overcome net discipline borders to analyse users’ well-being seems to be lacking. Today mainly qualitative data are used in the design field for the investigation of users’ quality experience. Although fundamental, they also have great disadvantages such as unanswered questions, unconscientious responses, and respondents’ biases. As such, a systematic state of art review is presented to find methodologies and tools currently used in medicine to identify the impact of digital design strategies (XR) on users’ well-being through quantitative and objective data. The main technologies used for this purpose have been synthesized in a schematic chart by reporting the principal related biometric data (skin conductivity, heart rate metrics and breathing rates), as well as other technologies such as video/images/audio analysis based on sensors and machine learning to reach out mass numbers. In conclusion, gaps and future applications of this innovative approach within the virtual environment have been identified by the authors

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Section: Identification Of the Main Stress Detection Parameters For S...mentioning

confidence: 99%

Section: Identification Of the Main Stress Detection Parameters For S...mentioning

confidence: 99%

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Application of Smart Technologies for Assessing Users’ Well-Being for Immersive Design Strategies: A State-of-the-Art Review

D'Ascenzi,

Getuli,

Fiesoli

2023

CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality

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“…A survey of 100 users was conducted in which better relaxation waveforms were observed in the case of yoga by 67% members and 29% readings showed better results in the case of music and 4% reported no results. Hand movement, heart rate variation, and EEG are used to analyze stress, and office syndrome was detected by intelligent watch in (Reanaree et al, 2016).…”

Section: Electroencephalography Based Stress Detectionmentioning

confidence: 99%

Mental Stress Detection using Data from Wearable and Non-wearable Sensors: A Review

Arsalan¹,

Anwar²,

Majid³

2022

Preprint

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This paper presents a comprehensive review of methods covering significant subjective and objective human stress detection techniques available in the literature. The methods for measuring human stress responses could include subjective questionnaires (developed by psychologists) and objective markers observed using data from wearable and non-wearable sensors. In particular, wearable sensor-based methods commonly use data from electroencephalography, electrocardiogram, galvanic skin response, electromyography, electrodermal activity, heart rate, heart rate variability, and photoplethysmography both individually and in multimodal fusion strategies. Whereas, methods based on non-wearable sensors include strategies such as analyzing pupil dilation and speech, smartphone data, eye movement, body posture, and thermal imaging. Whenever a stressful situation is encountered by an individual, physiological, physical, or behavioral changes are induced which help in coping with the challenge at hand. A wide range of studies has attempted to establish a relationship between these stressful situations and the response of human beings by using different kinds of psychological, physiological, physical, and behavioral measures. Inspired by the lack of availability of a definitive verdict about the relationship of human stress with these different kinds of markers, a detailed survey about human stress detection methods is conducted in this paper. In particular, we explore how stress detection methods can benefit from artificial intelligence utilizing relevant data from various sources. This review will prove to be a reference document that would

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“…Younger generations in their 20s are stressed every day owing to various academic, employment and office syndrome problems they face [1][2]. However, it is difficult for younger generations to be aware of the severity of mental stress on their health.…”

Section: Introductionmentioning

confidence: 99%

Effects of Daily Stress in Mental State Classification

Park

Dong

2020

IEEE Access

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An external stimulus, event, or environment that stresses an individual is called a stressor. Many mental stress detection studies have been focused on the discrimination of the mental state with and without the experimental stressor. However, the mental state in the absence of experimental stressors may not represent accurately the nonstress (baseline) state because people inherently experience considerable stress in their daily lives. Therefore, we assumed that stress detection could be improved more accurately by considering the daily stress. In this study, functional near-infrared spectroscopy (fNIRS) was measured in 41 healthy participants to quantify their prefrontal cortical oxygenation during performing a cognitive task as an experimental stressor, considering individual daily stress level based on the self-report. We then extracted six signal features, including the slope, mean, standard deviation, peak, skewness, and kurtosis of the oxygenated hemoglobin concentration. Using various feature combinations and time windows, we successfully managed to classify daily stress (high/low) and the mental state (task/rest) with support vector machine classifiers. Specifically, individual daily stress level can be easily discriminated with signal features from fNIRS. Moreover, mental state classification performance improved significantly when the daily stress level was handled separately. The findings of this study show the feasibility of the fNIRS-based daily stress classification and can be used in the future to design a robust mental stress management system for the assessment of daily stress in individuals.INDEX TERMS daily stress, functional near-infrared spectroscopy, stroop word color task, mental stress classification.

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Stress and office-syndrome detection using EEG, HRV and hand movement

Cited by 8 publications

References 6 publications

Application of Smart Technologies for Assessing Users’ Well-Being for Immersive Design Strategies: A State-of-the-Art Review

Application of Smart Technologies for Assessing Users’ Well-Being for Immersive Design Strategies: A State-of-the-Art Review

Mental Stress Detection using Data from Wearable and Non-wearable Sensors: A Review

Effects of Daily Stress in Mental State Classification

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