Mental stress detection using physiological signals based on soft computing techniques

Mokhayeri, Fania; Akbarzadeh-T, Mohammad-R.; Toosizadeh, Saeed

doi:10.1109/icbme.2011.6168563

Cited by 44 publications

(26 citation statements)

References 10 publications

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“…We calculated six statistical values (averages, standard deviations, first and second differences, and normalized first and second differences) [20] and the peak response time, peak amplitude, and energy [25] for EDA. We used the mean amplitude, skewness, and kurtosis [26] and the six statistical values of the signal for the blood volume pulse (BVP). Finally, we calculated the RMSSD [27], very low (0.05-0.15 Hz) and low (0.15-0.4 Hz) frequency bands and their ratio (low/very low), and the six statistical values for HR.…”

Section: Discussionmentioning

confidence: 99%

Stress Recognition in Daily Work

Nakashima

Kim

Flutura

et al. 2016

Communications in Computer and Information Science

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Abstract. Automatic detection of work-related stress has attracted an increasing amount of attention from researchers from various disciplines and industries. An experiment is discussed in this paper that was designed to evaluate the efficacy of multimodal sensor measures that have often been used but not yet been systematically tested and compared with each other in previous work, such as pressure distribution sensor, physiological sensors, and an eye tracker. We used the Stroop test and information pick up task as the stressors. In the subject independent case in particular, signals from the combined (chair and floor) pressure distribution sensors, which we consider the most feasible sensors in the office environment, resulted in higher recognition accuracy rates than the physiological or eye tracker signals for the two stressors.

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Section: Discussionmentioning

confidence: 99%

Stress Recognition in Daily Work

Nakashima

Kim

Flutura

et al. 2016

Communications in Computer and Information Science

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“…This group also attempted to classify the same two conditions using seating pressure data and obtained over 70% accuracy [9]. Mokhayeri et al used multi-modal physiological signals: pupil diameter, electrocardiogram and photoplethysmogram to classify stressed and relaxed conditions [7].…”

Section: Introductionmentioning

confidence: 99%

“…Several technologies have been developed to recognize stress level; some methods are based on physiological signals: blood pressure [1], heart rate [1], heart rate variability (HRV) [2], skin conductance [3,4], cortisol [5,6], pupil diameter [7]. Activity of sympathetic and para-sympathetic nervous system can be monitored through blood pressure, heart rate and HRV.…”

Section: Introductionmentioning

confidence: 99%

Stress Recognition Using Wearable Sensors and Mobile Phones

Sano¹,

Picard²

2013

2013 Humaine Association Conference on Affective Computing and Intelligent Interaction

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In this study, we aim to find physiological or behavioral markers for stress. We collected 5 days of data for 18 participants: a wrist sensor (accelerometer and skin conductance), mobile phone usage (call, short message service, location and screen on/off) and surveys (stress, mood, sleep, tiredness, general health, alcohol or caffeinated beverage intake and electronics usage). We applied correlation analysis to find statistically significant features associated with stress and used machine learning to classify whether the participants were stressed or not. In comparison to a baseline 87.5% accuracy using the surveys, our results showed over 75% accuracy in a binary classification using screen on, mobility, call or activity level information (some showed higher accuracy than the baseline). The correlation analysis showed that the higherreported stress level was related to activity level, SMS and screen on/off patterns.

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“…[2] Mokhayeri et al in their study used physiological signals which are multi-modal: Pupil diameter, photoplethysmogram, and electrocardiogram to distinguish relaxed and stressed conditions. [3] However, in some methods, surveys are used. [4] In this modern world, smartphones are showing many new features such as sensors (accelerometer, microphones, and GPS) and usage-tracking functions (SMS and call histories).…”

Section: Various Methods To Recognize Stress Levels In Studentsmentioning

confidence: 99%