EEG-based workers' stress recognition at construction sites

Jebelli, Houtan; Hwang, Sungjoo; Lee, Sang Hyun

doi:10.1016/j.autcon.2018.05.027

Cited by 246 publications

(112 citation statements)

References 31 publications

Supporting

Mentioning

110

Contrasting

Order By: Relevance

“…Other research groups have studied longitudinal EEG data from individual subjects such as an investigation involving 103-day single channel EEG data on emotion recognition in daily life [49]. Nowadays, fundamental knowledge on emotion recognition or affective computing using brain wave activity has been applied in broad areas such as studies of people with depression [50], stress in construction workers [51], and interactions between ambient temperature in building and the occupant [52].…”

Section: Consumer Grade Eeg For Emotion Recognitionmentioning

confidence: 99%

Consumer Grade Brain Sensing for Emotion Recognition

et al. 2019

View full text Add to dashboard Cite

For several decades, electroencephalography (EEG) has featured as one of the most commonly used tools in emotional state recognition via monitoring of distinctive brain activities. An array of datasets have been generated with the use of diverse emotion-eliciting stimuli and the resulting brainwave responses conventionally captured with high-end EEG devices. However, the applicability of these devices is to some extent limited by practical constraints and may prove difficult to be deployed in highly mobile context omnipresent in everyday happenings. In this study, we evaluate the potential of OpenBCI to bridge this gap by first comparing its performance to research grade EEG system, employing the same algorithms that were applied on benchmark datasets. Moreover, for the purpose of emotion classification, we propose a novel method to facilitate the selection of audio-visual stimuli of high/low valence and arousal. Our setup entailed recruiting 200 healthy volunteers of varying years of age to identify the top 60 affective video clips from a total of 120 candidates through standardized self assessment, genre tags, and unsupervised machine learning. Additional 43 participants were enrolled to watch the pre-selected clips during which emotional EEG brainwaves and peripheral physiological signals were collected. These recordings were analyzed and extracted features fed into a classification model to predict whether the elicited signals were associated with a high or low level of valence and arousal. As it turned out, our prediction accuracies were decidedly comparable to those of previous studies that utilized more costly EEG amplifiers for data acquisition.

show abstract

Section: Consumer Grade Eeg For Emotion Recognitionmentioning

confidence: 99%

Consumer Grade Brain Sensing for Emotion Recognition

et al. 2019

View full text Add to dashboard Cite

show abstract

“…The literature provided evidence for improving workplace mental health, such as alternate work schedules (Lingard et al, 2007); evidenced-based ideas for improvement (Eaves et al, 2016), and ways of early detection of stress (Jebelli et al 2018). Further research studies are required to build upon this literature in order to advance extant knowledge into how construction mental health problems can be identified and managed to produce a healthier workforce that ultimately, will reduce construction costs.…”

Section: Discussionmentioning

confidence: 99%

Mental Health in the Construction Industry: a Rapid Review

Borg¹,

Sherratt²,

Oswald³

2019

Proc. 27th Annual Conference of the International Group for Lean Construction (IGLC)

View full text Add to dashboard Cite

Principles of lean construction have been suggested as strategies towards improving health and safety (H&S) in construction. Previous research has typically focused on the physical aspects of H&S; yet individuals who work within the construction industry have an increased risk of suffering from mental health conditions. This is an area which is gaining attention but is yet to be researched in depth. Through a rapid review of literature, this paper identifies 15 studies, and reviews the current literature concerning mental health within the construction industry. Theoretically, results suggest that mental health is a significant problem within the construction industry; and practically, it is recommended that organisations should consider evidence-based suggestions of ways to address this issue through approaches such as alternate work schedules, and the use of validated detection tools. The results of the review revealed that the current literature is limited, both in terms of quantity and research approaches adopted, and therefore, further high-quality studies are required in this area. The principles of lean construction present an opportunity for addressing the issue of poor mental health in the construction industry, thus expanding the application of lean construction beyond addressing merely the physical aspects of H&S issues.

show abstract

“…In another research illustrated in [21], a technique has been proposed to consequently perceive laborers' stress in building locals. Authors had extracted features in frequency and time domain using fixed and sliding window techniques.…”

Section: Literature Reviewmentioning

confidence: 99%

Modified Support Vector Machine for Detecting Stress Level Using EEG Signals

Gupta

Alam

Agarwal

2020

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

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.

show abstract

EEG-based workers' stress recognition at construction sites

Cited by 246 publications

References 31 publications

Consumer Grade Brain Sensing for Emotion Recognition

Consumer Grade Brain Sensing for Emotion Recognition

Mental Health in the Construction Industry: a Rapid Review

Modified Support Vector Machine for Detecting Stress Level Using EEG Signals

Contact Info

Product

Resources

About