ReBreathe: A Calibration Protocol that Improves Stress/Relax Classification by Relabeling Deep Breathing Relaxation Exercises

Ahmed, Beena; Khan, H.; Choi, Jongyong; Gutierrez‐Osuna, Ricardo

doi:10.1109/taffc.2015.2459682

Cited by 17 publications

(7 citation statements)

References 48 publications

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“…As the relaxation activity in the protocol was deep breathing, where subjects controlled their respiratory rate, it was not used in the classifier to avoid biasing of the results. The output of the classifier was a metric which classified the mental state of the individual as being in either a relaxed class (0), low stress class (1) or high stress class (2). The input to the model was in the form of recorded physiological parameters which are mapped or labeled to three mental states or classes as provided by user's self-reported scores.…”

Section: Resultsmentioning

confidence: 99%

“…Several diseases such as diabetes, insomnia hypertension and depression can be caused by chronic stress [1]. Also a few social 1 Khalid Masood 1 , 2 Haroon Ur Rashid 1 University of Jeddah, 2 Lahore Garrison University 2 problems including family quarrels, marriage nullation, violence, suicides and 3 anger on streets and roads can be increased due to stress. To accurately diagnose chronic stress, physicians may want to observe a stressful person for long durations.…”

Section: Introductionmentioning

confidence: 99%

“…As stress is a psychological reaction between outer demands and environmental inputs that cross the barrier of a human capacity to sustain pressure [2], personal state monitoring includes not only monitoring the physiological reactions to stress but also the perceived psychological impacts of the stressors. Assessment of the physiological impact can be reported with limited wearable sensors that record physiological signals including changes in heart rate, variations in respiration rate and changes in conductance of the skin.…”

Section: Introductionmentioning

confidence: 99%

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Cognitive Experiments and Features for Computing Mental Stress

Masood

Rashid

2017

Research Journal Of Computer Science And Information Technology

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In this paper, mental stress is computed through cognitive experiments that induce stress. In a controlled laboratory environment, a group of students are involved in a series of mental challenges. While performing the cognitive tasks, stress is induced on the participants. Deep breathing exercise is performed in the start of experiments and then in between each activity to make the conditions normal and a participant feels relaxed. Various physiological features are recorded during experimental activities. Also, cerebral features are recorded that provide improved classification results. The severity of stress is different on each participant but the purpose of experimental protocol is to separate stressful conditions from relaxed environment. Support Vector machine (SVM) is used to identify relax or normal class from a number of stressed classes. It is shown that cerebral features improve the classification accuracy with a satisfactory margin and designed protocol system is able to compute the severity of induced stress.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Cognitive Experiments and Features for Computing Mental Stress

Masood

Rashid

2017

Research Journal Of Computer Science And Information Technology

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“…The breath rate of a person is increased under stressful conditions, whereas on the contrary it is decreased in calm situation (Vinkers et al, 2013;McDuff et al, 2014;Grossman, 1983). Stress is associated with the irregularities of the respiratory rate (Singh and Queyam, 2013b), the shift from abdominal to thoracic breathing (Ahmed et al, 2015), and faster and shallower breathing (Kreibig, 2010). The breath rate sensor has been reported to be the accurate estimation of the respiratory rate.…”

Section: Respiratory Rate 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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“…To assess CWL, researchers typically use surveys [7], performance metrics [8], [9], and information from physiological signals [10]. However, surveys only provide subjective and sporadic measurements, and are not always reliable [11]. Although performance metrics provide objective measurements, reliable metrics are difficult to set as every rescue mission is unique.…”

Section: Introductionmentioning

confidence: 99%

Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions With Drones

Dell’Agnola

Jao

Arza

et al. 2022

IEEE J. Biomed. Health Inform.

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In search and rescue missions, drone operations are challenging and cognitively demanding. High levels of cognitive workload can affect rescuers' performance, leading to failure with catastrophic outcomes. To face this problem, we propose a machine learning algorithm for real-time cognitive workload monitoring to understand if a search and rescue operator has to be replaced or if more resources are required. Our multimodal cognitive workload monitoring model combines the information of 25 features extracted from physiological signals, such as respiration, electrocardiogram, photoplethysmogram, and skin temperature, acquired in a noninvasive way. To reduce both subject and day inter-variability of the signals, we explore different feature normalization techniques, and introduce a novel weighted-learning method based on support vector machines suitable for subject-specific optimizations. On an unseen test set acquired from 34 volunteers, our proposed subject-specific model is able to distinguish between low and high cognitive workloads with an average accuracy of 87.3% and 91.2% while controlling a drone simulator using both a traditional controller and a new-generation controller, respectively.

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ReBreathe: A Calibration Protocol that Improves Stress/Relax Classification by Relabeling Deep Breathing Relaxation Exercises

Cited by 17 publications

References 48 publications

Cognitive Experiments and Features for Computing Mental Stress

Cognitive Experiments and Features for Computing Mental Stress

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

Machine-Learning Based Monitoring of Cognitive Workload in Rescue Missions With Drones

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