A Wearable System for Stress Detection Through Physiological Data Analysis

There is a rich repertoire of methods for stress detection using various physiological signals and algorithms. However, there is still a gap in research efforts moving from laboratory studies to real-world settings. A small number of research has verified when a physiological response is a reaction to an extrinsic stimulus of the participant’s environment in real-world settings. Typically, physiological signals are correlated with the spatial characteristics of the physical environment, supported by video records or interviews. The present research aims to bridge the gap between laboratory settings and real-world field studies by introducing a new algorithm that leverages the capabilities of wearable physiological sensors to detect moments of stress (MOS). We propose a rule-based algorithm based on galvanic skin response and skin temperature, combing empirical findings with expert knowledge to ensure transferability between laboratory settings and real-world field studies. To verify our algorithm, we carried out a laboratory experiment to create a “gold standard” of physiological responses to stressors. We validated the algorithm in real-world field studies using a mixed-method approach by spatially correlating the participant’s perceived stress, geo-located questionnaires, and the corresponding real-world situation from the video. Results show that the algorithm detects MOS with 84% accuracy, showing high correlations between measured (by wearable sensors), reported (by questionnaires and eDiary entries), and recorded (by video) stress events. The urban stressors that were identified in the real-world studies originate from traffic congestion, dangerous driving situations, and crowded areas such as tourist attractions. The presented research can enhance stress detection in real life and may thus foster a better understanding of circumstances that bring about physiological stress in humans.

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“…This correlation is similar in both test cities. More details on the qualitative conclusions can be found in [82].…”

Section: Real-world Field Studiesmentioning

confidence: 99%

Detecting Moments of Stress from Measurements of Wearable Physiological Sensors

Kyriakou

Resch

Sagl

et al. 2019

Sensors

162

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“…Electrodermal activities, as a prime indicator of stress and mental workload, have been measured in different domains such as healthcare (Ritz et al, 2000), transportation (Mehler et al, 2012; Ruscio et al, 2017; Schneegass et al, 2013), psychology (Blechert et al, 2006; Nomikos et al, 1968), and engineering (Acerbi et al, 2017). Most of the previous research establishes that phasic EDA increases significantly in a stressful or demanding situation (Giannakakis et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Quantifying Occupational Stress in Intensive Care Unit Nurses: An Applied Naturalistic Study of Correlations Among Stress, Heart Rate, Electrodermal Activity, and Skin Temperature

et al. 2021

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Objective To identify physiological correlates to stress in intensive care unit nurses. Background Most research on stress correlates are done in laboratory environments; naturalistic investigation of stress remains a general gap. Method Electrodermal activity, heart rate, and skin temperatures were recorded continuously for 12-hr nursing shifts (23 participants) using a wrist-worn wearable technology (Empatica E4). Results Positive correlations included stress and heart rate (ρ = .35, p < .001), stress and skin temperature (ρ = .49, p < .05), and heart rate and skin temperatures (ρ = .54, p = .0008). Discussion The presence and direction of some correlations found in this study differ from those anticipated from prior literature, illustrating the importance of complementing laboratory research with naturalistic studies. Further work is warranted to recognize nursing activities associated with a high level of stress and the underlying reasons associated with changes in physiological responses. Application Heart rate and skin temperature may be used for real-time detection of stress, but more work is needed to validate such surrogate measures.

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“…A low HRV can indicate a relaxed state, whereas a high one represents a www.ijacsa.thesai.org potential state of stress or depression. While signal power [13] in low frequency (LF: 0.04-0.15Hz) and high frequency (HF: 0.15-0.4Hz) bands also appear to have a close relationship with emotions. Fig.…”

Section: B Related Workmentioning

confidence: 99%

Emotional Changes Detection for Dementia People with Spectrograms from Physiological Signals

Zeng¹,

Peijia²,

Iwamoto³

et al. 2018

ijacsa

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Due to aging society, there has recently been an increasing percentage of people with serious cognitive decline and dementia around the world. Such patients often lose their diversity of facial expressions and even their ability to speak, rendering them unable to express their feelings to their caregivers. However, emotions and feelings are strongly correlated with physiological signals, detectable with EEG and ECG etc. Therefore, this research develops an emotion predicting system for people with dementia using bio-signals to support their interaction with their caregivers. In this paper, we focused on a previous study for binary classification of emotional changes using spectrograms of EEG and RRI by CNN, verifying the effectiveness of the method. Firstly, the participants were required to watch simulating videos while collecting their EEG and ECG data. Then, STFT was performed, processing the raw data signals by extracting the time-frequency domain features to get the spectrograms. Finally, deep learning was used to detect the emotional changes. CNN was used for arousal classification, with an accuracy of 90.00% with EEG spectrograms, 91.67% with RRI spectrograms, and 93.33% with EEG and RRI spectrograms.

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A Wearable System for Stress Detection Through Physiological Data Analysis

Cited by 33 publications

References 33 publications

Detecting Moments of Stress from Measurements of Wearable Physiological Sensors

Detecting Moments of Stress from Measurements of Wearable Physiological Sensors

Quantifying Occupational Stress in Intensive Care Unit Nurses: An Applied Naturalistic Study of Correlations Among Stress, Heart Rate, Electrodermal Activity, and Skin Temperature

Emotional Changes Detection for Dementia People with Spectrograms from Physiological Signals

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