Electrodermal Activity Based Wearable Device for Drowsy Drivers

Malathi, D.; Jayaseeli, J. D. Dorathi; Madhuri, S; Senthilkumar, K.

doi:10.1088/1742-6596/1000/1/012048

Cited by 23 publications

(19 citation statements)

References 19 publications

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“…EDA is connected with autonomous nervous system, specifically with sympathicus [88], which influences activity of sweat glands. There exist two types of EDA signalsexosomatic and endosomatic [88], [89]. Commonly measured exosomatic is an impedance of the skin caused by sweating; in this case the source of constant current is used and the skin conductance changes are measured as a result of sweating [89].…”

Section: ) Electrodermal Activitymentioning

confidence: 99%

“…There exist two types of EDA signalsexosomatic and endosomatic [88], [89]. Commonly measured exosomatic is an impedance of the skin caused by sweating; in this case the source of constant current is used and the skin conductance changes are measured as a result of sweating [89]. Endosomatic EDA is measured by Ag/AgCl electrodes and in this case the electrical activity of nervous pulses which activate the sweat glands is measured; the skin potentials are constant and the measured current changes [89].…”

Section: ) Electrodermal Activitymentioning

confidence: 99%

“…Skin conductance has two components -tonic (Skin Conductance Level, SCL) and phasic (Skin Conductance Response, SCR). SCL is connected with basic activity of sweat glands without any stimuli presented and is characterized by slow changes (from 10 s to tens of minutes) [89]. SCR changes faster with coming stimuli and lasts for a shorter period (typically 1 -5 s) [89].…”

Section: ) Electrodermal Activitymentioning

confidence: 99%

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Multimodal Features for Detection of Driver Stress and Fatigue: Review

Němcová

Svozilová

Bucsuházy

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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Driver fatigue and stress significantly contribute to higher number of car accidents worldwide. Although, different detection approaches have been already commercialized and used by car producers (and third party companies), research activities in this field are still needed in order to increase the reliability of these alert systems. Also, in the context of automated driving, the driver mental state assessment will be an important part of cars in future. This paper presents state-of-the-art review of different approaches for driver fatigue and stress detection and evaluation. We describe in details various signals (biological, car and video) and derived features used for these tasks and we discuss their relevance and advantages. In order to make this review complete, we also describe different datasets, acquisition systems and experiment scenarios.

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Section: ) Electrodermal Activitymentioning

confidence: 99%

Section: ) Electrodermal Activitymentioning

confidence: 99%

Section: ) Electrodermal Activitymentioning

confidence: 99%

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Multimodal Features for Detection of Driver Stress and Fatigue: Review

Němcová

Svozilová

Bucsuházy

et al. 2021

IEEE Trans. Intell. Transport. Syst.

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show abstract

“…In Eq. 3, ∈ {0,1} chooses either a tonic or a phasic atom, ∈ and ∈ (1) or ∈ (2) or ∈ (3) were the parameters of ℎ atom. Then, the phasic atoms were grouped according to their location with a histogram of values to capture one SCR as:…”

Section: Overview Of Existing Models I) Sparse Decomposition Modelmentioning

confidence: 99%

“…Generally, emotions like stress, anger, fear, etc., are controlled by the sympathetic nervous system. Electrodermal Activity (EDA) refers to a phenomenon where the changes in emotion levels of a person directly reflect their psychological state [1]. EDA is also known as Skin Conductance Level (SCL), SC Response (SCR), ED Response (EDR), etc., and can provide a noninvasive, easily acquired, robust and economical approach for recording physiological data.…”

Section: Introductionmentioning

confidence: 99%

A Modified Compressed Sensing-Based Decomposition Approach to Analyze Electrodermal Activity Signals Using Matrix-Free Convex Optimization

Prabhakaran¹,

Ayyamperumal²

2020

IJIES

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In modern decades, a human socioeconomic lifestyle has been disrupted by their stress levels. Normally, the stress levels were measured based on the Electrodermal Activity (EDA) analysis that analyses the changes in the Skin Conductance Response (SCR) level. As a result, different sensors were designed to analyze the changes in human emotional levels using the EDA signals. During analysis, the unwanted noise components in the signals were removed by using a Compressed Sensing-based Decomposition (CSD) approach. Conversely, this approach has high computational complexity. Therefore in this article, a Modified CSD (MCSD) for EDA signal is proposed that may vary the shape of the impulse response with time and varied noise models. The key aim of this model is to improve the recovery accuracy of EDA signal decomposition and enhance the human stress monitoring system. In this modified model, a computationally efficient decomposition method is proposed by using matrix-free convex optimization modeling that exploits the Toeplitz structure to allow decomposition of EDA signals with provable bounds on the recovery of true SCR events by using the computationally efficient algorithm and guarantee a better recovery accuracy. Finally, this approach is tested on Wearable Stress and Affect Detection (WESAD) dataset to exhibit that the proposed MCSD approach achieves a recovery accuracy of 92.4% which is 2.55% higher than the existing CSD approach.

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Smartwatch-Based Wearable and Usable System for Driver Drowsiness Detection

Misbhauddin

2020

Innovations in Smart Cities Applications Edition 3

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Electrodermal Activity Based Wearable Device for Drowsy Drivers

Cited by 23 publications

References 19 publications

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Multimodal Features for Detection of Driver Stress and Fatigue: Review

A Modified Compressed Sensing-Based Decomposition Approach to Analyze Electrodermal Activity Signals Using Matrix-Free Convex Optimization

Smartwatch-Based Wearable and Usable System for Driver Drowsiness Detection

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