Danger, high voltage! Using EEG and EOG measurements for cognitive overload detection in a simulated industrial context

Morton, Jessica; Zheleva, Aleksandra; Acker, Bram Boris Van; Durnez, Wouter; Vanneste, Pieter; Larmuseau, Charlotte; Bruyne, Jonas De; Raes, Annelies; Cornillie, Frederik; Saldien, Jelle; Marez, Lieven De; Bombeke, Klaas

doi:10.1016/j.apergo.2022.103763

Cited by 12 publications

(5 citation statements)

References 71 publications

(119 reference statements)

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“…However, this phenomenon was only observed in this study when comparing low- and high-complexity conditions and medium- and high-complexity conditions. Similarly, another study found that individual alpha frequencies from EEG and blink rate from EOG can discriminate between medium, high, and overloaded cognitive-load levels in the puzzle-solving assembly task [ 17 ]. However, in this study, the cognitive-workload indices could not distinguish complexity well between low and medium conditions.…”

Section: Discussionmentioning

confidence: 99%

“…Three general methods exist to measure cognitive workload. First, self-reported and performance inference [ 17 , 18 ] are the most widely used methods in ergonomics; these two methods are generally accepted and can be applied in most cases. Along with the recent development of smart sensors, some studies have used physiological measurement methods [ 3 , 17 , 19 , 20 ] in the field of applied ergonomics, which reflects the desire to obtain more objective and precise measures of cognitive workload.…”

Section: Introductionmentioning

confidence: 99%

“…Several studies have investigated the cognitive workload of operators in assembly tasks. The individual alpha frequency from EEG and blink rate from electrooculography (EOG) were shown to discriminate the cognitive-load levels between medium, high, and overload in the context of puzzle-related assembly tasks [ 17 ]. A vision-based cognitive-load assessment was also developed with a stereo camera under the 3D-printed objects’ assembly task [ 20 ].…”

Section: Introductionmentioning

confidence: 99%

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Determining Cognitive Workload Using Physiological Measurements: Pupillometry and Heart-Rate Variability

Ma,

Monfared,

Grant

et al. 2024

Sensors

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The adoption of Industry 4.0 technologies in manufacturing systems has accelerated in recent years, with a shift towards understanding operators’ well-being and resilience within the context of creating a human-centric manufacturing environment. In addition to measuring physical workload, monitoring operators’ cognitive workload is becoming a key element in maintaining a healthy and high-performing working environment in future digitalized manufacturing systems. The current approaches to the measurement of cognitive workload may be inadequate when human operators are faced with a series of new digitalized technologies, where their impact on operators’ mental workload and performance needs to be better understood. Therefore, a new method for measuring and determining the cognitive workload is required. Here, we propose a new method for determining cognitive-workload indices in a human-centric environment. The approach provides a method to define and verify the relationships between the factors of task complexity, cognitive workload, operators’ level of expertise, and indirectly, the operator performance level in a highly digitalized manufacturing environment. Our strategy is tested in a series of experiments where operators perform assembly tasks on a Wankel Engine block. The physiological signals from heart-rate variability and pupillometry bio-markers of 17 operators were captured and analysed using eye-tracking and electrocardiogram sensors. The experimental results demonstrate statistically significant differences in both cardiac and pupillometry-based cognitive load indices across the four task complexity levels (rest, low, medium, and high). Notably, these developed indices also provide better indications of cognitive load responding to changes in complexity compared to other measures. Additionally, while experts appear to exhibit lower cognitive loads across all complexity levels, further analysis is required to confirm statistically significant differences. In conclusion, the results from both measurement sensors are found to be compatible and in support of the proposed new approach. Our strategy should be useful for designing and optimizing workplace environments based on the cognitive load experienced by operators.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Determining Cognitive Workload Using Physiological Measurements: Pupillometry and Heart-Rate Variability

Ma,

Monfared,

Grant

et al. 2024

Sensors

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“…On the other hand, cognitive tasks aim to replicate high levels of attention and mental concentration in real manufacturing contexts. Examples are the N-back task [13,19], [28,29], the auditory stimulus detection task [11], or the visual search task [17]. Motor tasks are instead proposed to measure physical workload, while cognitive tasks are usually related to mental workload; however, standard tasks have still not emerged.…”

Section: Literature Reviewmentioning

confidence: 99%

Workload and stress evaluation in advanced manufacturing systems

Blandino

2023

Materials Research Proceedings

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Abstract. Industry 5.0 emphasizes the development of human-centred work environments, shifting the focus from technologies embedded in manufacturing systems to workers. Efforts in the literature focus on operators' well-being for workstation configuration or on stress in collaborative environments, but few papers consider stress induced by management practices in advanced manufacturing contexts, although “lean” or “agile” for instance could in principle lead to more stressful workplaces. This paper reviews the literature, evaluating the mental and physical workload of production line operators who perform mentally demanding tasks and experience stress in advanced manufacturing systems. The goal is to design and to perform a pilot test on an innovative and rigorous research protocol, to be adopted in ‘non-fictional’ experiments, and able to compare push vs pull settings and their effects on workers’ workload and stress (WLS). The results will highlight new sources of stress, contributing to the development of human-centred and socially sustainable manufacturing systems.

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“…The manufacturing environment should also provide VOLUME 11, 2023 high-value tasks to maintain worker engagement while minimizing the cognitive workload to keep workers mentally healthy. Mental workload is a subjective parameter influenced greatly by an individual's cognitive capabilities at different tasks that could lead not only to errors, safety risks, and performance inefficiency [10] but also has negative effects on workers' behaviour, motivation and well-being [11].…”

Section: Human Factors At Workplacementioning

confidence: 99%

A Comprehensive Study of Human Factors, Sensory Principles, and Commercial Solutions for Future Human-Centered Working Operations in Industry 5.0

et al. 2023

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The purpose of this study is to explore the measurement of human factors in the workplace that can provide critical insights into workers' well-being. Human factors refer to physical, cognitive, and psychological states that can impact the efficiency, effectiveness, and mental health of workers. The article identifies six human factors that are particularly crucial in today's workplaces: physical fatigue, attention, mental workload, stress, trust, and emotional state. Each of these factors alters the human physiological response in a unique way, affecting the human brain, cardiovascular, electrodermal, muscular, respiratory, and ocular reactions. This paper provides an overview of these human factors and their specific influence on psycho-physiological responses, along with suitable technologies to measure them in working environments and the currently available commercial solutions to do so. By understanding the importance of these human factors, employers can make informed decisions to create a better work environment that leads to improved worker well-being and productivity.INDEX TERMS Human factors, industry 5.0, psycho-physiological signals, human physical and cognitive states, signal acquisition, measuring techniques, human-in-the-loop, industrial applications.

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Danger, high voltage! Using EEG and EOG measurements for cognitive overload detection in a simulated industrial context

Cited by 12 publications

References 71 publications

Determining Cognitive Workload Using Physiological Measurements: Pupillometry and Heart-Rate Variability

Determining Cognitive Workload Using Physiological Measurements: Pupillometry and Heart-Rate Variability

Workload and stress evaluation in advanced manufacturing systems

A Comprehensive Study of Human Factors, Sensory Principles, and Commercial Solutions for Future Human-Centered Working Operations in Industry 5.0

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