Revealing the “Invisible Gorilla” in construction: Estimating construction safety through mental workload assessment

Chen, Jiayu; Song, Xinyi; Zhao, Liguo

doi:10.1016/j.autcon.2015.12.018

Cited by 147 publications

(70 citation statements)

References 55 publications

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“…Figure 1 illustrates the literature search and study selection process. Ninety-one eligible studies [3,18,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69,70,71,72,73,74,75,76,77,78,79,80,81,82,83,84,85,86,87,…”

Section: Resultsmentioning

confidence: 99%

A Systematic Review of Physiological Measures of Mental Workload

Tao

Tan

Wang

et al. 2019

IJERPH

148

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Mental workload (MWL) can affect human performance and is considered critical in the design and evaluation of complex human-machine systems. While numerous physiological measures are used to assess MWL, there appears no consensus on their validity as effective agents of MWL. This study was conducted to provide a comprehensive understanding of the use of physiological measures of MWL and to synthesize empirical evidence on the validity of the measures to discriminate changes in MWL. A systematical literature search was conducted with four electronic databases for empirical studies measuring MWL with physiological measures. Ninety-one studies were included for analysis. We identified 78 physiological measures, which were distributed in cardiovascular, eye movement, electroencephalogram (EEG), respiration, electromyogram (EMG) and skin categories. Cardiovascular, eye movement and EEG measures were the most widely used across varied research domains, with 76%, 66%, and 71% of times reported a significant association with MWL, respectively. While most physiological measures were found to be able to discriminate changes in MWL, they were not universally valid in all task scenarios. The use of physiological measures and their validity for MWL assessment also varied across different research domains. Our study offers insights into the understanding and selection of appropriate physiological measures for MWL assessment in varied human-machine systems.

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Section: Resultsmentioning

confidence: 99%

A Systematic Review of Physiological Measures of Mental Workload

Tao

Tan

Wang

et al. 2019

IJERPH

148

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“…Scientific studies present the application of commercial devices for the evaluation of workers in the construction industry [10,11,69,70,72]. Likewise, the use of more complex devices such as electroencephalograms (EEG) to measure the brain activity [10,68] or to evaluate workers actions/conduct/behavior. Craft workers are the CI "athletes" and the researchers should apply all knowledge available to achieve their highest performance.…”

Section: Sensored Construction Sitesmentioning

confidence: 99%

Worker 4.0: The Future of Sensored Construction Sites

Calvetti

Mêda²,

Gonçalves

et al. 2020

Buildings

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The digitalization of the construction industry (CI) has the aim—among others—to raise the bar of overall productivity. The craft workforce is very relevant on the overall value-chain. Therefore, a boost in this dimension impacts the entire sector. There is a gap in proper methodologies to measure and model productivity. Construction 4.0 novelties provide new approaches for its evaluation and progress. This communication presents a review of workforce productivity assessment and delivers methods focusing primarily on craft workers motion monitoring. Products and services opportunities from Construction 4.0 in the spectrum of craft workforce management include support by embedded sensors for data collection that allow near real-time monitoring. The work developed led to the systematization of a framework to standardize craft workers’ motion productivity. The craft workforce motion productivity framework, Worker 4.0, tenders nine processes integrated on a flowchart to streamline task processes assessment and mechanization level. It also sets up a two-handed/two-legged chart system to model craft workers’ activities and operations. The contributions to the body of knowledge are substantiated on the framework creation with the ability to model and assess craft workforce performance. This approach is meant to serve as base point for different stakeholders focusing on skills, efficiency, mechanization and productivity improvements.

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“…Based on EEG data and therapist's evaluation during driving skill training, Fan et al established a group-level classification model to distinguish the emotional states and MW of patients in the autism spectrum disorder during driving skill training [20]. Chen et al demonstrated the validity of EEG signals in evaluating the MW of construction workers and described the development of a wearable EEG safety helmet prototype [21]. Puma et al used EEG signals to assess the increase in MW in multitasking environments.…”

Section: Related Workmentioning

confidence: 99%

Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders

Yang

Yin

Wang

et al. 2019

Computers in Biology and Medicine

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To estimate the reliability and cognitive states of operator performance in a human-machine collaborative environment, we propose a novel human mental workload (MW) recognizer based on deep learning principles and utilizing the features of the electroencephalogram (EEG). To determine personalized properties in high dimensional EEG indicators, we introduce a feature mapping layer in stacked denoising autoencoder (SDAE) that is capable of preserving the local information in EEG dynamics. The ensemble classifier is then built via the subjectspecific integrated deep learning committee, and adapts to the cognitive properties of a specific human operator and alleviates inter-subject feature variations. We validate our algorithms and the ensemble SDAE classifier with local information preservation (denoted by EL-SDAE) on an EEG database collected during the execution of complex human-machine tasks. The classification performance indicates that the EL-SDAE outperforms several classical MW estimators when its optimal network architecture has been identified.

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Revealing the “Invisible Gorilla” in construction: Estimating construction safety through mental workload assessment

Cited by 147 publications

References 55 publications

A Systematic Review of Physiological Measures of Mental Workload

A Systematic Review of Physiological Measures of Mental Workload

Worker 4.0: The Future of Sensored Construction Sites

Assessing cognitive mental workload via EEG signals and an ensemble deep learning classifier based on denoising autoencoders

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