Pupil diameter size marker for incremental mental stress detection

Torres-Salomao, L. A.; Mahfouf, Mahdi; Elsamahy, E.

doi:10.1109/healthcom.2015.7454513

Cited by 14 publications

(8 citation statements)

References 24 publications

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“…Facial temperatures are based on infrared imaging recordings from an FLIR E40bx thermal imaging camera at a sampling frequency of 10 Hz. The data recording methods follow the experimental frameworks in [12], [14]- [16], [29], [30].…”

Section: B Data Acquisitionmentioning

confidence: 99%

“…The mental arithmetic applied in this research is based on the MATLAB ® GUI app similar to the app used in [15], [16], [29], [30]. The participants in this research are ten (10) healthy students from the University of Sheffield aged from 22 to 30.…”

Section: B Data Acquisitionmentioning

confidence: 99%

“…The dilation and constriction of the pupil are dominated by the sympathetic and parasympathetic nerves from the autonomic nervous system [34]. Therefore, the pupil diameter marker P DM has been recommended as a credible indicator for psychophysiological state estimation in HMI studies [29], [34], [35]. In this research, the generated pupil size is the mean value from 30-second image frameworks.…”

Section: Psychophysiological Biomarkersmentioning

confidence: 99%

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An Adaptive General Type-2 Fuzzy Logic Approach for Psychophysiological State Modeling in Real-Time Human–Machine Interfaces

Mahfouf

Torres-Salomao³

2021

IEEE Trans. Human-Mach. Syst.

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Section: B Data Acquisitionmentioning

confidence: 99%

Section: B Data Acquisitionmentioning

confidence: 99%

Section: Psychophysiological Biomarkersmentioning

confidence: 99%

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An Adaptive General Type-2 Fuzzy Logic Approach for Psychophysiological State Modeling in Real-Time Human–Machine Interfaces

Mahfouf

Torres-Salomao³

2021

IEEE Trans. Human-Mach. Syst.

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“…In other studies, EDA and HRV are also integrated with EEG [25], physical activity data based on 3D accelerometer [26], video-based activity data [27], facial expressions [28], and speech analysis [29]. Other research studies have investigated different biosignals as stress detection markers, such as pupil diameter [30], and eye gaze [31]. Thermography through thermal camera or IR touchscreen has been used to extract stress-related markers, such as breathing [32], facial blood flow changes [33], and photoplethysmographic (PPG) signal [34], [35].…”

Section: B Stress Monitoring and Detection Systemsmentioning

confidence: 99%

Cognitive Training and Stress Detection in MCI Frail Older People Through Wearable Sensors and Machine Learning

2020

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Personalised training of motor and cognitive abilities is fundamental to help older people maintain a good quality of life, especially in case of frailty conditions. However, the training activity can increase the stress level, especially in persons affected by a chronic stress condition. Wearable technologies and m-health solutions can support the person, the medical specialist, and long-term care facilities to efficiently implement personalised therapy solutions by monitoring the stress level of each subject during the motor and cognitive training. In this paper we present a comprehensive work on this topic, starting from a pilot study involving a group of frail older adults suffering from Mild Cognitive Impairment (MCI) who actively participated in cognitive and motor rehabilitation sessions equipped with wearable physiological sensors and a mobile application for physiological monitoring. We analyse the collected data to investigate the stress response of frail older subjects during the therapy, and how the cognitive training is positively affected by physical exercise. Then, we evaluated a stress detection system based on several machine learning algorithms in order to highlight their performances on the real dataset we collected. However, stress detection algorithms generally provide only the identification of a stressful/non stressful event, which is not sufficient to personalise the therapy. Therefore, we propose a mobile system architecture for online stress monitoring able to infer the stress level during a session. The obtained result is then used as input for a Decision Support System (DSS) in order to support the medical user in the definition of a personalised therapy for frail older adults.

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“…The physiological parameters affected by the SNS have been used in a lot of studies for stress detection, among which are: Salivary Cortisol [22], respiratory signs [23,24], skin temperature and thermal image [25][26][27], variability of heart rate (HRV) [28][29][30], electrocardiogram (ECG) [15,31,32], electroencephalogram (EEG) [20,33,34], Galvanic Skin Response (GSR) [35][36][37], pupil diameter [38,39], electromyogram (EMG) [40][41][42], and the Volatile Organic Compounds (VOC's) emitted by the skin [43][44][45]. The response focus of each one of these physiological signals depends on the context since these signals can show variations due to physical activity.…”

Section: Introductionmentioning

confidence: 99%

Electronic Devices for Stress Detection in Academic Contexts during Confinement Because of the COVID-19 Pandemic

2021

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This article studies the development and implementation of different electronic devices for measuring signals during stress situations, specifically in academic contexts in a student group of the Engineering Department at the University of Pamplona (Colombia). For the research’s development, devices for measuring physiological signals were used through a Galvanic Skin Response (GSR), the electrical response of the heart by using an electrocardiogram (ECG), the electrical activity produced by the upper trapezius muscle (EMG), and the development of an electronic nose system (E-nose) as a pilot study for the detection and identification of the Volatile Organic Compounds profiles emitted by the skin. The data gathering was taken during an online test (during the COVID-19 Pandemic), in which the aim was to measure the student’s stress state and then during the relaxation state after the exam period. Two algorithms were used for the data process, such as Linear Discriminant Analysis and Support Vector Machine through the Python software for the classification and differentiation of the assessment, achieving 100% of classification through GSR, 90% with the E-nose system proposed, 90% with the EMG system, and 88% success by using ECG, respectively.

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Pupil diameter size marker for incremental mental stress detection

Cited by 14 publications

References 24 publications

An Adaptive General Type-2 Fuzzy Logic Approach for Psychophysiological State Modeling in Real-Time Human–Machine Interfaces

An Adaptive General Type-2 Fuzzy Logic Approach for Psychophysiological State Modeling in Real-Time Human–Machine Interfaces

Cognitive Training and Stress Detection in MCI Frail Older People Through Wearable Sensors and Machine Learning

Electronic Devices for Stress Detection in Academic Contexts during Confinement Because of the COVID-19 Pandemic

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