Assessment of Implicit and Explicit Measures of Mental Workload in Working Situations: Implications for Industry 4.0

Mingardi, Michele; Pluchino, Patrik; Bacchin, Davide; Rossato, Chiara; Gamberini, Luciano

doi:10.3390/app10186416

Cited by 10 publications

(12 citation statements)

References 48 publications

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“…Indeed, the physiological response to a stimulus does not have a univocal meaning. This result confirms what we found in the literature: the biometric measures reflect different internal states (e.g., variations in the arousal, the valence, the level of stress, or mental workload) during the execution of a task [54]. In particular, the proposed method concretely distinguishes between the mental effort and the emotional involvement of the operator during working activities.…”

Section: Cognitive Assessmentsupporting

confidence: 90%

“…With regard to the eye-related metrics, variations due to a high level of MWL are present in pupil diameter (increment), fixation frequency (decrement), and saccadic frequency (decrement). Opposing results about fixation duration, blink duration and frequency are available in the literature [54][55][56]. For this reason, in this work, the focus is on the pupil diameter.…”

Section: The Proposed Methods For Operators' Workload Assessmentmentioning

confidence: 99%

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A Preliminary Experimental Study on the Workers’ Workload Assessment to Design Industrial Products and Processes

et al. 2021

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The human-centered design (HCD) approach places humans at the center of design in order to improve both products and processes, and to give users an effective, efficient and satisfying interactive experience. In industrial design and engineering, HCD is very useful in helping to achieve the novel Industry 5.0 concept, based on improving workers’ wellbeing by providing prosperity beyond jobs and growth, while respecting the production limits of the planet as recently promoted by the European Commission. In this context, the paper proposes an ergonomic assessment method based on the analysis of the workers’ workload to support the design of industrial products and processes. This allows the simultaneous analysis of the physical and cognitive workload of operators while performing their tasks during their shift. The method uses a minimum set of non-invasive wearable devices to monitor human activity and physiological parameters, in addition to questionnaires for subjective self-assessment. The method has been preliminarily tested on a real industrial case in order to demonstrate how it can help companies to support the design of optimized products and processes promoting the workers’ wellbeing.

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Section: Cognitive Assessmentsupporting

confidence: 90%

Section: The Proposed Methods For Operators' Workload Assessmentmentioning

confidence: 99%

A Preliminary Experimental Study on the Workers’ Workload Assessment to Design Industrial Products and Processes

et al. 2021

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“…Pupil variation is linked to the sympathetic nervous system, which is involved in arousal and wakefulness (Mathot et al 2018). Therefore, how pupil size variations are modulated by operations with one or the other interface can provide information about the level of implicit workload within a joint operation (Mingardi et al 2020). It is worth noting that many variables other than the user's cognitive and emotional state (e.g., ambient lighting) can affect this metric (Kramer 2020).…”

Section: Metrics For the Assessment Of Human Factors In Hrcsmentioning

confidence: 99%

The virtualization of human–robot interactions: a user-centric workload assessment

et al. 2022

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Interest in the virtualization of human–robot interactions is increasing, yet the impact that collaborating with either virtual or physical robots has on the human operator’s mental state is still insufficiently studied. In the present work, we aimed to fill this gap by conducting a systematic assessment of a human–robot collaborative framework from a user-centric perspective. Mental workload was measured in participants working in synergistic co-operation with a physical and a virtual collaborative robot (cobot) under different levels of task demands. Performance and implicit and explicit workload were assessed as a function of pupil size variation and self-reporting questionnaires. In the face of a similar self-reported mental demand when maneuvering the virtual or physical cobot, operators showed shorter operation times and lower implicit workload when interacting with the virtual cobot compared to its physical counterpart. Furthermore, the benefits of collaborating with a virtual cobot most vividly manifested when the user had to position the robotic arm with higher precision. These results shed light on the feasibility and importance of relying on multidimensional assessments in real-life work settings, including implicit workload predictors such as pupillometric measures. From a broader perspective, our findings suggest that virtual simulations have the potential to bring significant advantages for both the user's mental well-being and industrial production, particularly for highly complex and demanding tasks.

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“…In our study, by analyzing the TLX, it could be also possible to assess subscale scores affected by the mental workload related to a specific task. In experiments where mental demand is increased, participants mentioned higher perceived mental demand, effort, and frustration, with lower subjective perception of their task performance (Mingardi et al, 2020 ). The task duration of 20 s is required to acquire that data to generate the commands, and around 1 s is needed to execute that command.…”

Section: Discussionmentioning

confidence: 99%

“…MWL is proposed as a specific task, so that even under stress and cognitive load, the patients could generate control command signals to operate the exoskeleton, and the study gauge its operational effectiveness. Moreover, MWL commands may be efficiently gauged with NASA TLX (Mansikka et al, 2019 ; Mingardi et al, 2020 ). This study assessed cognitive load using National Aeronautics and Space Administration's Task Load Index (NASA TLX), a multi-dimensional assessment method with six sub-scales: including mental demands, performance, effort, and frustration (Felton et al, 2012 ).…”

Section: Introductionmentioning

confidence: 99%

Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain–Machine Interface (BMI)

et al. 2021

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Mental workload is a neuroergonomic human factor, which is widely used in planning a system's safety and areas like brain–machine interface (BMI), neurofeedback, and assistive technologies. Robotic prosthetics methodologies are employed for assisting hemiplegic patients in performing routine activities. Assistive technologies' design and operation are required to have an easy interface with the brain with fewer protocols, in an attempt to optimize mobility and autonomy. The possible answer to these design questions may lie in neuroergonomics coupled with BMI systems. In this study, two human factors are addressed: designing a lightweight wearable robotic exoskeleton hand that is used to assist the potential stroke patients with an integrated portable brain interface using mental workload (MWL) signals acquired with portable functional near-infrared spectroscopy (fNIRS) system. The system may generate command signals for operating a wearable robotic exoskeleton hand using two-state MWL signals. The fNIRS system is used to record optical signals in the form of change in concentration of oxy and deoxygenated hemoglobin (HbO and HbR) from the pre-frontal cortex (PFC) region of the brain. Fifteen participants participated in this study and were given hand-grasping tasks. Two-state MWL signals acquired from the PFC region of the participant's brain are segregated using machine learning classifier—support vector machines (SVM) to utilize in operating a robotic exoskeleton hand. The maximum classification accuracy is 91.31%, using a combination of mean-slope features with an average information transfer rate (ITR) of 1.43. These results show the feasibility of a two-state MWL (fNIRS-based) robotic exoskeleton hand (BMI system) for hemiplegic patients assisting in the physical grasping tasks.

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Assessment of Implicit and Explicit Measures of Mental Workload in Working Situations: Implications for Industry 4.0

Cited by 10 publications

References 48 publications

A Preliminary Experimental Study on the Workers’ Workload Assessment to Design Industrial Products and Processes

A Preliminary Experimental Study on the Workers’ Workload Assessment to Design Industrial Products and Processes

The virtualization of human–robot interactions: a user-centric workload assessment

Motor Training Using Mental Workload (MWL) With an Assistive Soft Exoskeleton System: A Functional Near-Infrared Spectroscopy (fNIRS) Study for Brain–Machine Interface (BMI)

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