Mental Workload Monitoring: New Perspectives from Neuroscience

Babiloni, Fabio

doi:10.1007/978-3-030-32423-0_1

Cited by 24 publications

(38 citation statements)

References 109 publications

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“…Among all the HFs, stress, mental overload, and lack of vigilance could cause tragic human errors in several working environments [ 12 , 13 , 14 ]. Giving the limitations imposed by subjective evaluation of mental states [ 15 , 16 , 17 ] and due to the fact that in some specific activities it is not possible to interrupt operators while working, researchers started to acquire biosignals to monitor and assess operators’ mental states. Biomarkers such as skin conductance level (SCL), heart rate (HR), and eye blink rate (EBR) are investigated as correlates of users’ mental states to develop a monitoring system to diminish and prevent fatal and non-fatal accidents [ 4 , 6 , 16 , 18 , 19 , 20 ].…”

Section: Introductionmentioning

confidence: 99%

Wearable Technologies for Mental Workload, Stress, and Emotional State Assessment during Working-Like Tasks: A Comparison with Laboratory Technologies

Giorgi¹,

Ronca

Vozzi

et al. 2021

Sensors

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The capability of monitoring user’s performance represents a crucial aspect to improve safety and efficiency of several human-related activities. Human errors are indeed among the major causes of work-related accidents. Assessing human factors (HFs) could prevent these accidents through specific neurophysiological signals’ evaluation but laboratory sensors require highly-specialized operators and imply a certain grade of invasiveness which could negatively interfere with the worker’s activity. On the contrary, consumer wearables are characterized by their ease of use and their comfortability, other than being cheaper compared to laboratory technologies. Therefore, wearable sensors could represent an ideal substitute for laboratory technologies for a real-time assessment of human performances in ecological settings. The present study aimed at assessing the reliability and capability of consumer wearable devices (i.e., Empatica E4 and Muse 2) in discriminating specific mental states compared to laboratory equipment. The electrooculographic (EOG), electrodermal activity (EDA) and photoplethysmographic (PPG) signals were acquired from a group of 17 volunteers who took part to the experimental protocol in which different working scenarios were simulated to induce different levels of mental workload, stress, and emotional state. The results demonstrated that the parameters computed by the consumer wearable and laboratory sensors were positively and significantly correlated and exhibited the same evidences in terms of mental states discrimination.

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Section: Introductionmentioning

confidence: 99%

Wearable Technologies for Mental Workload, Stress, and Emotional State Assessment during Working-Like Tasks: A Comparison with Laboratory Technologies

Giorgi¹,

Ronca

Vozzi

et al. 2021

Sensors

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“…In contrast, estimations of players’ cognitive or emotional states based on in-game metrics ( Nebel and Ninaus, 2019 ), might be used to adapt systems to increase training effectiveness, performance, and motivation. Among different affective and cognitive components, cognitive load seems to be particularly interesting as it is considered to reflect the degree to which available cognitive resources are engaged in the task at hand ( Babiloni, 2019 ). As Gerjets et al (2014) pointed out, the actual level of cognitive load is relevant in a variety of realistic settings, such as adaptive learning environments, where optimal learning content is characterized by an intermediate level of cognitive load.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, even though the research on cognitive load has a long history ( Linton et al, 1978 ; Welford, 1978 ; Sheridan and Simpson, 1979 ; Eggemeier et al, 1985 ; Meshkati, 1988 ; Sweller et al, 1998 ; Barrouillet et al, 2004 ) it’s still a scientifically vibrant field of interest given its crucial importance for everyday life. As noted by Babiloni (2019) in his recent review on the topic, cognitive load can be characterized by a complex interplay between different task demands and a variety of mental processes such as alertness, vigilance, fatigue, etc., and thus represents a result of a complex interaction of different aspects. That is, cognitive load is a dynamic variable that may change rapidly during task processing.…”

Section: Introductionmentioning

confidence: 99%

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Measuring Cognitive Load Using In-Game Metrics of a Serious Simulation Game

et al. 2021

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Serious games have become an important tool to train individuals in a range of different skills. Importantly, serious games or gamified scenarios allow for simulating realistic time-critical situations to train and also assess individual performance. In this context, determining the user’s cognitive load during (game-based) training seems crucial for predicting performance and potential adaptation of the training environment to improve training effectiveness. Therefore, it is important to identify in-game metrics sensitive to users’ cognitive load. According to Barrouillets’ time-based resource-sharing model, particularly relevant for measuring cognitive load in time-critical situations, cognitive load does not depend solely on the complexity of actions but also on temporal aspects of a given task. In this study, we applied this idea to the context of a serious game by proposing in-game metrics for workload prediction that reflect a relation between the time during which participants’ attention is captured and the total time available for the task at hand. We used an emergency simulation serious game requiring management of time-critical situations. Forty-seven participants completed the emergency simulation and rated their workload using the NASA-TLX questionnaire. Results indicated that the proposed in-game metrics yielded significant associations both with subjective workload measures as well as with gaming performance. Moreover, we observed that a prediction model based solely on data from the first minutes of the gameplay predicted overall gaming performance with a classification accuracy significantly above chance level and not significantly different from a model based on subjective workload ratings. These results imply that in-game metrics may qualify for a real-time adaptation of a game-based learning environment.

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“…Among the various forms of cognitive load, MWL is of central importance as it influences the operators’ performance and thus the system performance [ 21 ]. MWL is a complex construct and is challenging to define accurately [ 22 ], however MWL is assumed to be a reflection of the level of cognitive engagement and effort as an operator performs one or more tasks. Henceforth, a general definition of MWL is “the relationship between the function relating the mental resources demanded by a task and those resources available to be supplied by the human operator” [ 23 ].…”

Section: Introductionmentioning

confidence: 99%

A Cyber-Physical-Human System for One-to-Many UAS Operations: Cognitive Load Analysis

Planke

Lim

Gardi

et al. 2020

Sensors

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The continuing development of avionics for Unmanned Aircraft Systems (UASs) is introducing higher levels of intelligence and autonomy both in the flight vehicle and in the ground mission control, allowing new promising operational concepts to emerge. One-to-Many (OTM) UAS operations is one such concept and its implementation will require significant advances in several areas, particularly in the field of Human–Machine Interfaces and Interactions (HMI2). Measuring cognitive load during OTM operations, in particular Mental Workload (MWL), is desirable as it can relieve some of the negative effects of increased automation by providing the ability to dynamically optimize avionics HMI2 to achieve an optimal sharing of tasks between the autonomous flight vehicles and the human operator. The novel Cognitive Human Machine System (CHMS) proposed in this paper is a Cyber-Physical Human (CPH) system that exploits the recent technological developments of affordable physiological sensors. This system focuses on physiological sensing and Artificial Intelligence (AI) techniques that can support a dynamic adaptation of the HMI2 in response to the operators’ cognitive state (including MWL), external/environmental conditions and mission success criteria. However, significant research gaps still exist, one of which relates to a universally valid method for determining MWL that can be applied to UAS operational scenarios. As such, in this paper we present results from a study on measuring MWL on five participants in an OTM UAS wildfire detection scenario, using Electroencephalogram (EEG) and eye tracking measurements. These physiological data are compared with a subjective measure and a task index collected from mission-specific data, which serves as an objective task performance measure. The results show statistically significant differences for all measures including the subjective, performance and physiological measures performed on the various mission phases. Additionally, a good correlation is found between the two physiological measurements and the task index. Fusing the physiological data and correlating with the task index gave the highest correlation coefficient (CC = 0.726 ± 0.14) across all participants. This demonstrates how fusing different physiological measurements can provide a more accurate representation of the operators’ MWL, whilst also allowing for increased integrity and reliability of the system.

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Mental Workload Monitoring: New Perspectives from Neuroscience

Cited by 24 publications

References 109 publications

Wearable Technologies for Mental Workload, Stress, and Emotional State Assessment during Working-Like Tasks: A Comparison with Laboratory Technologies

Wearable Technologies for Mental Workload, Stress, and Emotional State Assessment during Working-Like Tasks: A Comparison with Laboratory Technologies

Measuring Cognitive Load Using In-Game Metrics of a Serious Simulation Game

A Cyber-Physical-Human System for One-to-Many UAS Operations: Cognitive Load Analysis

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