On identification of driving-induced stress using electroencephalogram signals: A framework based on wearable safety-critical scheme and machine learning

Halim, Zahid; Rehan, Mahma

doi:10.1016/j.inffus.2019.06.006

Cited by 172 publications

(88 citation statements)

References 64 publications

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“…Time features extracted from raw EEG signals were used. Halim and Rehan [100] published a study on subjects who underwent driving-related tasks in laboratory conditions. Several EEG features were tested time domain (EEG average, standard deviation, 1 st and 2 nd differences etc.)…”

Section: ) Brain Activitymentioning

confidence: 99%

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: ) Brain Activitymentioning

confidence: 99%

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Němcová

Svozilová

Bucsuházy

et al. 2021

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…In [ 22 ], a machine learning-based approach has been proposed to deal with driving-actuated stress. They had used 3 classifiers, namely, SVM, neural networks, and Random Forest, to classify the EEG signals collected from the subjects.…”

Section: Literature Reviewmentioning

confidence: 99%

Modified Support Vector Machine for Detecting Stress Level Using EEG Signals

Gupta

Alam

Agarwal

2020

Computational Intelligence and Neuroscience

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Stress is categorized as a condition of mental strain or pressure approaches because of upsetting or requesting conditions. There are various sources of stress initiation. Researchers consider human cerebrum as the primary wellspring of stress. To study how each individual encounters stress in different forms, researchers conduct surveys and monitor it. The paper presents the fusion of 5 algorithms to enhance the accuracy for detection of mental stress using EEG signals. The Whale Optimization Algorithm has been modified to select the optimal kernel in the SVM classifier for stress detection. An integrated set of algorithms (NLM, DCT, and MBPSO) has been used for preprocessing, feature extraction, and selection. The proposed algorithm has been tested on EEG signals collected from 14 subjects to identify the stress level. The proposed approach was validated using accuracy, sensitivity, specificity, and F1 score with values of 96.36%, 96.84%, 90.8%, and 97.96% and was found to be better than the existing ones. The algorithm may be useful to psychiatrists and health consultants for diagnosing the stress level.

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“…Apart from these, there exist some linguistic‐based works on judicial documents, as in Halim, Ali, and Khan (2020), Halim and Rehan (2020) Metsker, Trofimov, Sikorsky, and Kovalchuk (2018), Tang and Kageura (2019) and Yuan, Lan, Hao, and Zhao (2019). These practical applications ensured strong motivation for research in the judicial text.…”

Section: Introductionmentioning

confidence: 99%

Semi‐supervised, knowledge‐integrated pattern learning approach for fact extraction from judicial text

Thomas

Sangeetha

2021

Expert Systems

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Tremendous growth in the availability of judicial documents has demanded the rise of information extraction (IE) techniques that support the automatic extraction of relevant concepts or data from judicial texts. Among various approaches available for IE, ontology‐based IE has proven to be the most appropriate for extracting domain‐specific information from natural language text. Through this article, we propose a knowledge‐driven, semi‐supervised pattern‐based learning (bootstrapping) approach to extract domain‐specific facts from judicial text, starting with a small set of seed facts. In the semantic analysis of legal text, fact extraction is the next step to entity identification, which involves the identification of roles played by each entity in the judicial text. The proposed methodology learns extraction patterns for 12 classes of facts from the judicial text through the integration of the domain ontology called judicial case ontology (JCO). The experimental results were evaluated by human judges and found to be quite promising. One main feature of the proposed methodology is its portability across various domains (such as medical, banking, insurance, etc.) which in turn helps build expert systems in various sectors.

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On identification of driving-induced stress using electroencephalogram signals: A framework based on wearable safety-critical scheme and machine learning

Cited by 172 publications

References 64 publications

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Multimodal Features for Detection of Driver Stress and Fatigue: Review

Modified Support Vector Machine for Detecting Stress Level Using EEG Signals

Semi‐supervised, knowledge‐integrated pattern learning approach for fact extraction from judicial text

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