A multimodal Physiological-Based Stress Recognition: Deep Learning Models’ Evaluation in Gamers’ Monitoring Application

Dhaouadi, Sabrine; Khelifa, Mohamed Moncef Ben

doi:10.1109/atsip49331.2020.9231666

Cited by 16 publications

(11 citation statements)

References 13 publications

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“…In this study, we demonstrated the use of DNN in the classification of multivariate physiological data. In particular, a key aspect of our study is that it focuses on six different types of physiological signals, instead of a limited number of physiological signals like similar studies have done [ 61 , 62 , 63 ].…”

Section: Discussionmentioning

confidence: 99%

Deep Neural Networks and Transfer Learning on a Multivariate Physiological Signal Dataset

2021

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While Deep Neural Networks (DNNs) and Transfer Learning (TL) have greatly contributed to several medical and clinical disciplines, the application to multivariate physiological datasets is still limited. Current examples mainly focus on one physiological signal and can only utilise applications that are customised for that specific measure, thus it limits the possibility of transferring the trained DNN to other domains. In this study, we composed a dataset (n=813) of six different types of physiological signals (Electrocardiogram, Electrodermal activity, Electromyogram, Photoplethysmogram, Respiration and Acceleration). Signals were collected from 232 subjects using four different acquisition devices. We used a DNN to classify the type of physiological signal and to demonstrate how the TL approach allows the exploitation of the efficiency of DNNs in other domains. After the DNN was trained to optimally classify the type of signal, the features that were automatically extracted by the DNN were used to classify the type of device used for the acquisition using a Support Vector Machine. The dataset, the code and the trained parameters of the DNN are made publicly available to encourage the adoption of DNN and TL in applications with multivariate physiological signals.

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Section: Discussionmentioning

confidence: 99%

Deep Neural Networks and Transfer Learning on a Multivariate Physiological Signal Dataset

2021

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“…This method can be grouped into two classes of signals which are cerebral (e.g., electroencephalography, functional magnetic resonance) and peripheral (e.g., biological activity, heart rate, temperature). 19 The physiological signals for instance can be heart rate variability (HRV), electrocardiography (ECG), electromyography (EMG), electroencephalography (EEG), galvanic skin response (GSR), photoplethysmography (PPG). 1…”

Section: Physiological Signals As Stress Markersmentioning

confidence: 99%

“…The HRV can be obtained from assessing the variation of time in milliseconds between two heartbeats (also known as RR interval). 19…”

Section: Electrocardiography (Ecg) Photoplethysmography (Ppg) and Hea...mentioning

confidence: 99%

“…31 The action potential of the EMG signals, shows higher amplitude when the muscles are exposed to stressors or stressful events compared to resting. 19 Apart from that, the EEG can measure brain activity in relation to stress. 32 It is an instrument to monitor and measure the variations of electrical activities in the brain.…”

Section: Electromyography (Emg) and Electroencephalography (Eeg)mentioning

confidence: 99%

“…Despite its widespread use in healthcare, it does have significant disadvantages. ECG device, according to 33 Chen et al, 19 is not ideal for daily usage because they are expensive, inconvenient, and only widely available in healthcare facilities such as the hospitals and not readily accessible to the general population and determining patients' stress levels throughout the day.…”

Section: The Pros and Cons Of Physiological Signalsmentioning

confidence: 99%

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Stress Monitoring Device and its Future Direction

Amid

Mahpodz

Abdullah

2023

imjm

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Stress and mental health have become a global concern as a result of the COVID-19 pandemic. This review highlights the technologies applied to stress monitoring with up-to -date literature on psychological and behavioural evaluation for stress detection. The working principle and potential of these stress identifiers, particularly physiological signals are explored. Researchers have been directing their interest in producing reliable and wearable devices to detect and prevent stress and panic attacks. The breakthrough of biochemical-near-infrared stress monitoring devices will facilitate medical practitioners in the early detection of stress and panic attacks. The review seeks to explore the types of methods used to detect stress as well as the pros and cons of the available technology while providing a new solution for future implications of stress monitoring detection devices.

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