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

Abstract

Nowadays, in the context of Industry 4.0, advanced working environments aim at achieving a high degree of human–machine collaboration. This phenomenon occurs, on the one hand, through the correct interpretation of operators’ data by machines that can adapt their functioning to support workers, and on the other hand, by ensuring the transparency of the actions of the system itself. This study used an ad hoc system that allowed the co-registration of a set of participants’ implicit and explicit (I/E) data in two experimental conditions that varied in the level of mental workload (MWL). Findings showed that the majority of the considered I/E measures were able to discriminate the different task-related mental demands and some implicit measures were capable of predicting task performance in both tasks. Moreover, self-reported measures showed that participants were aware of such differences in MWL. Finally, the paradigm’s ecology highlights that task and environmental features may affect the reliability of the various I/E measures. Thus, these factors have to be considered in the design and development of advanced adaptive systems within the industrial context.

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

“…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.…”

“…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.…”

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

“…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).…”

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

“…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.…”

“…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 ).…”