Review on Psychological Stress Detection Using Biosignals

Giannakakis, Giorgos; Grigoriadis, Dimitris; Giannakaki, Katerina; Simantiraki, Olympia; Roniotis, Alexandros; Tsiknakis, Manolis

doi:10.1109/taffc.2019.2927337

Cited by 434 publications

(327 citation statements)

References 239 publications

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“…They could only achieve a 0.43 F1 score (with three classes) and listed the additional difficulties of working in the wild while explaining the reasons for the relatively low performance [32]. The performance of daily life stress monitoring systems is lower than studies conducted in controlled environments due to the mentioned issues such as low-quality physiological signals with artifacts and the difficulty of collecting the ground truth in [33,34] (see Table 1).…”

Section: Related Workmentioning

confidence: 99%

“…As mentioned, we had two types of class labels in the laboratory environment stress detection experiment: PSS-5 self-reports and the known context (the stressor level). On the other hand, in the wild, we had only self-reports of individuals, which was among the reasons why daily life stress detection performances were low [34], and there is still a room for improvement in daily life stress detection research.…”

Section: Testing the Models In The Wildmentioning

confidence: 99%

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How Laboratory Experiments Can Be Exploited for Monitoring Stress in the Wild: A Bridge Between Laboratory and Daily Life

Can

Gokay

Kılıç

et al. 2020

Sensors

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Chronic stress leads to poor well-being, and it has effects on life quality and health. Society may have significant benefits from an automatic daily life stress detection system using unobtrusive wearable devices using physiological signals. However, the performance of these systems is not sufficiently accurate when they are used in unrestricted daily life compared to the systems tested in controlled real-life and laboratory conditions. To test our stress level detection system that preprocesses noisy physiological signals, extracts features, and applies machine learning classification techniques, we used a laboratory experiment and ecological momentary assessment based data collection with smartwatches in daily life. We investigated the effect of different labeling techniques and different training and test environments. In the laboratory environments, we had more controlled situations, and we could validate the perceived stress from self-reports. When machine learning models were trained in the laboratory instead of training them with the data coming from daily life, the accuracy of the system when tested in daily life improved significantly. The subjectivity effect coming from the self-reports in daily life could be eliminated. Our system obtained higher stress level detection accuracy results compared to most of the previous daily life studies.

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Section: Related Workmentioning

confidence: 99%

Section: Testing the Models In The Wildmentioning

confidence: 99%

How Laboratory Experiments Can Be Exploited for Monitoring Stress in the Wild: A Bridge Between Laboratory and Daily Life

Can

Gokay

Kılıç

et al. 2020

Sensors

View full text Add to dashboard Cite

show abstract

“…Stress detection models based on physiological signals allow us to objectively monitor the driver’s stress level in real time. They mainly use the heart rate signal, skin conductance, skin temperature and the encephalogram [ 41 , 42 ]. The main disadvantage of these methods is that they require the use of sensors, which increases the cost and reduces the number of potential participants.…”

Section: Related Work On Stress Detectionmentioning

confidence: 99%

The Effects of the Driver’s Mental State and Passenger Compartment Conditions on Driving Performance and Driving Stress

Magaña

Scherz²,

Seepold

et al. 2020

Sensors

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Globalization has increased the number of road trips and vehicles. The result has been an intensification of traffic accidents, which are becoming one of the most important causes of death worldwide. Traffic accidents are often due to human error, the probability of which increases when the cognitive ability of the driver decreases. Cognitive capacity is closely related to the driver’s mental state, as well as other external factors such as the CO2 concentration inside the vehicle. The objective of this work is to analyze how these elements affect driving. We have conducted an experiment with 50 drivers who have driven for 25 min using a driving simulator. These drivers completed a survey at the start and end of the experiment to obtain information about their mental state. In addition, during the test, their stress level was monitored using biometric sensors and the state of the environment (temperature, humidity and CO2 level) was recorded. The results of the experiment show that the initial level of stress and tiredness of the driver can have a strong impact on stress, driving behavior and fatigue produced by the driving test. Other elements such as sadness and the conditions of the interior of the vehicle also cause impaired driving and affect compliance with traffic regulations.

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“…The VAD decision is based on frame energy value E f , frame zero-crossing measure value ZC f , threshold energy value E th , and zero-crossing threshold value ZC th , as presented in Eq. (6). The proposed VAD algorithm detects voiced speech frames, since only in these frames of the speech signal, the pitch value can be determined.…”

Section: Voice Activity Detection Algorithmmentioning

confidence: 99%

“…Besides the direct text input, the ASR and its submodules can provide the NLP with additional meta-information, which can be used to improve the virtual agents' response to the user's communication. Some categories of such meta-information are emotions, stress level [6], effects of spontaneous speech, speakers' change [7]. As an example, change of speaker influences dialogue modelling, which can be seen as an essential part of language generation with NLP approaches.…”

Section: Introductionmentioning

confidence: 99%

Effective Pitch Value Detection in Noisy Intelligent Environments for Efficient Natural Language Processing

Vlaj¹,

Žgank²,

Kos³

2020

Recent Trends in Computational Intelligence

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The performance of applications based on natural language processing depends primarily on the environment in which these applications are applied. Intelligent environments will be one of the major applications used to process natural language. The methods for speaker's gender classification can adapt and improve the performance of natural language processing applications. That is why, this chapter will present an effective speaker's pitch value detection in noisy environments, which then allows more robust speaker's gender classification. The chapter presents the algorithm for the speaker's pitch value detection and performs the comparison in various noisy environments. The experiments are carried out on the part of the publically available Aurora 2 speech database. The results showed that the automatically determined pitch values deviate, on average, only by 8.39 Hz from the reference pitch value. A well-defined pitch value allows a functional speaker's gender classification. In this chapter, presented speaker's gender classification works well, even at low signal to noise ratios. The experiments show that the speaker's gender classification performance at SNR 0 dB is higher than 91% when the automatically determined pitch value is used. Speaker's gender classification can then be used further in the processes of natural language processing.

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Review on Psychological Stress Detection Using Biosignals

Cited by 434 publications

References 239 publications

How Laboratory Experiments Can Be Exploited for Monitoring Stress in the Wild: A Bridge Between Laboratory and Daily Life

How Laboratory Experiments Can Be Exploited for Monitoring Stress in the Wild: A Bridge Between Laboratory and Daily Life

The Effects of the Driver’s Mental State and Passenger Compartment Conditions on Driving Performance and Driving Stress

Effective Pitch Value Detection in Noisy Intelligent Environments for Efficient Natural Language Processing

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