Toward soft real-time stress detection using wrist-worn devices for human workspaces

Khowaja, Sunder Ali; Prabono, Aria Ghora; Setiawan, Feri; Yahya, Bernardo Nugroho; Lee, Seok-Lyong

doi:10.1007/s00500-020-05338-0

Cited by 32 publications

(25 citation statements)

References 94 publications

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“…However, in achieving communitywide benefit the emotional distress needs to be diagnosed in time. Mental health disorders such as emotional distress and stress can be measured with wearable sensors including electrocardiograph (ECG), GSR, and electroencephalogram (EEG) [21,40,41]. Some of the smartwatches listed in Table 1 comprise of such sensors that could help in recognizing the emotions of an individual, accurately.…”

Section: Mental Healthcarementioning

confidence: 99%

VIRFIM: an AI and Internet of Medical Things-driven framework for healthcare using smart sensors

Khowaja

Khuwaja

Dev

et al. 2021

Neural Comput & Applic

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After affecting the world in unexpected ways, the virus has started mutating which is evident with the insurgence of its new variants. The governments, hospitals, schools, industries, and humans, in general, are looking for a potential solution in the vaccine which will eventually be available, but its timeline for eradicating the virus is yet unknown. Several researchers have encouraged and recommended the use of good practices such as physical healthcare monitoring, immunity boosting, personal hygiene, mental healthcare, and contact tracing for slowing down the spread of the virus. In this article, we propose the use of smart sensors integrated with the Internet of Medical Things to cover the spectrum of good practices in an automated manner. We present hypothetical frameworks for each of the good practice modules and propose the VIrus Resistance Framework using the Internet of Medical Things (VIRFIM) to tie all the individual modules in a unified architecture. Furthermore, we validate the realization of VIRFIM framework with two case studies related to physical activity monitoring and stress detection services. We envision that VIRFIM would be influential in assisting people with the new normal for current and future pandemics as well as instrumental in halting the economic losses, respectively. We also provide potential challenges and their probable solutions in compliance with the proposed VIRFIM.

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Section: Mental Healthcarementioning

confidence: 99%

VIRFIM: an AI and Internet of Medical Things-driven framework for healthcare using smart sensors

Khowaja

Khuwaja

Dev

et al. 2021

Neural Comput & Applic

Self Cite

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“…In stress recognition studies, feature extraction has been performed mainly in the time or frequency domain of physiological signals. Time domain features were typically extracted from time series segments truncated by window sliding strategies [2,6,[23][24][25][26][27][28], whereas frequency domain features were extracted from low-and/or high-frequency regions [6,[25][26][27]29,30]. Based on these features, statistical measures such as mean, standard deviation, skewness and kurtosis were commonly calculated and used to differentiate between stressed and non-stressed conditions [24].…”

Section: Introductionmentioning

confidence: 99%

“…Particularly, in driving situations, stress is closely related to driving safety. For example, stress can lead to traffic accidents by impairing driving performance or reducing a driver’s ability to make decisions to cope with dangerous situations [ 5 , 6 ]. Thus, the problem of recognizing stress early has been tackled in several studies to reduce the possibility of traffic accidents [ 7 , 8 , 9 ].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, nonlinear features, such as recurrence quantification analysis (RQA) [ 33 ] measurements, were employed in some stress recognition studies [ 6 , 34 , 35 ]. RQA measurements are to quantify the structure of a recurrence plot (RP) [ 36 ] representing the recurrence properties of time series data presented in the phase space trajectory.…”

Section: Introductionmentioning

confidence: 99%

“…However, ECGs are inconvenient and suffer from noise vulnerability from unstable contacts. Nowadays the leveraging of short-term (i.e., tens of seconds long) GSR or HR signals is increasing, but still knowledge-based feature engineering accompanied with conventional machine learning classifiers requires substantial time and human efforts [ 2 , 6 , 14 , 28 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals

Lee

Shin

2021

Sensors

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Mental stress can lead to traffic accidents by reducing a driver’s concentration or increasing fatigue while driving. In recent years, demand for methods to detect drivers’ stress in advance to prevent dangerous situations increased. Thus, we propose a novel method for detecting driving stress using nonlinear representations of short-term (30 s or less) physiological signals for multimodal convolutional neural networks (CNNs). Specifically, from hand/foot galvanic skin response (HGSR, FGSR) and heart rate (HR) short-term input signals, first, we generate corresponding two-dimensional nonlinear representations called continuous recurrence plots (Cont-RPs). Second, from the Cont-RPs, we use multimodal CNNs to automatically extract FGSR, HGSR, and HR signal representative features that can effectively differentiate between stressed and relaxed states. Lastly, we concatenate the three extracted features into one integrated representation vector, which we feed to a fully connected layer to perform classification. For the evaluation, we use a public stress dataset collected from actual driving environments. Experimental results show that the proposed method demonstrates superior performance for 30-s signals, with an overall accuracy of 95.67%, an approximately 2.5–3% improvement compared with that of previous works. Additionally, for 10-s signals, the proposed method achieves 92.33% classification accuracy, which is similar to or better than the performance of other methods using long-term signals (over 100 s).

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Using Wearable Devices for Emotion Recognition in Mobile Human- Computer Interaction: A Review

Wang¹,

Zhang²

2022

HCI International 2022 - Late Breaking Papers. Multimodality in Advanced Interaction Environments

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Toward soft real-time stress detection using wrist-worn devices for human workspaces

Cited by 32 publications

References 94 publications

VIRFIM: an AI and Internet of Medical Things-driven framework for healthcare using smart sensors

VIRFIM: an AI and Internet of Medical Things-driven framework for healthcare using smart sensors

Driving Stress Detection Using Multimodal Convolutional Neural Networks with Nonlinear Representation of Short-Term Physiological Signals

Using Wearable Devices for Emotion Recognition in Mobile Human- Computer Interaction: A Review

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