Workload Alerts—Using Physiological Measures of Mental Workload to Provide Feedback During Tasks

Maior, Horia A.; Wilson, Max L.; Sharples, Sarah

doi:10.1145/3173380

Cited by 34 publications

(15 citation statements)

References 48 publications

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“…In symbiotic systems, these users' collected data could be shared among all the computational devices connected through the network. Once it detects the operator's need for support (e.g., an extreme level of stress), the system could tailor its interface and functions by implementing real-time adaptive features [7,8]. These concepts were modeled by Gamberini and Spagnolli ([3]; see Figure 1).…”

Section: Introductionmentioning

confidence: 99%

“…To consider the critical aspects of the symbiotic system mentioned above, we needed to identify a reliable construct that could be assessed based on implicit, explicit, and subjective measures [16]. Within the work context [8], the impact of mental workload (MWL) on performance has been analyzed [17][18][19], especially regarding its optimization for users operating in the environment of so-called Industry 4.0 [20].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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“…Another significant use of PFC asymmetry has been early NF experiments, primarily for the treatment of depression (Rosenfeld et al, 1995). PFC asymmetry has been later adopted as a BCI technology taking advantage of the above results, and has been used for affective computing (Mühl and Heylen, 2009) cognitive workload measurement (Fishburn et al, 2014;Peck et al, 2014;Barth et al, 2016;Maior et al, 2020) or assessment of aesthetic response (Karran et al, 2015;Cartocci et al, 2016).…”

Section: Pfc Asymmetry In Neuroscience Researchmentioning

confidence: 99%

Prefrontal Asymmetry BCI Neurofeedback Datasets

2020

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Prefrontal cortex (PFC) asymmetry is an important marker in affective neuroscience and has attracted significant interest, having been associated with studies of motivation, eating behavior, empathy, risk propensity, and clinical depression. The data presented in this paper are the result of three different experiments using PFC asymmetry neurofeedback (NF) as a Brain-Computer Interface (BCI) paradigm, rather than a therapeutic mechanism aiming at long-term effects, using functional near-infrared spectroscopy (fNIRS) which is known to be particularly well-suited to the study of PFC asymmetry and is less sensitive to artifacts. From an experimental perspective the BCI context brings more emphasis on individual subjects' baselines, successful and sustained activation during epochs, and minimal training. The subject pool is also drawn from the general population, with less bias toward specific behavioral patterns, and no inclusion of any patient data. We accompany our datasets with a detailed description of data formats, experiment and protocol designs, as well as analysis of the individualized metrics for definitions of success scores based on baseline thresholds as well as reference tasks. The work presented in this paper is the result of several experiments in the domain of BCI where participants are interacting with continuous visual feedback following a real-time NF paradigm, arising from our long-standing research in the field of affective computing. We offer the community access to our fNIRS datasets from these experiments. We specifically provide data drawn from our empirical studies in the field of affective interactions with computer-generated narratives as well as interfacing with algorithms, such as heuristic search, which all provide a mechanism to improve the ability of the participants to engage in active BCI due to their realistic visual feedback. Beyond providing details of the methodologies used where participants received real-time NF of left-asymmetric increase in activation in their dorsolateral prefrontal cortex (DLPFC), we re-establish the need for carefully designing protocols to ensure the benefits of NF paradigm in BCI are enhanced by the ability of the real-time visual feedback to adapt to the individual responses of the participants. Individualized feedback is paramount to the success of NF in BCIs.

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“…In our prior work, we have anecdotally observed the difference between participants who enjoy a task, and those that are worried or stressed about achieving them [11], where stressed participants tended to perform worse. It has been suggested that Selye coined the term 'stress' in 1936 [15].…”

Section: Introductionmentioning

confidence: 98%

How Stress Affects Functional Near-Infrared Spectroscopy (fNIRS) Measurements of Mental Workload

Alsuraykh

Maior

Wilson

et al. 2018

Extended Abstracts of the 2018 CHI Conference on Human Factors in Computing Systems

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Recent work has demonstrated that functional Near-Infrared Spectroscopy has the potential to measure changes in Mental Workload with increasing ecological validity. It is not clear, however, whether these measurements are affected by anxiety and stress of the workload, where our informal observations see some participants enjoying the workload and succeeding in tasks, while others worry and struggle with the tasks. This research evaluated the effects of stress on fNIRS measurements and performance, using the Montreal Imaging Stress Task to manipulate the experience of stress. While our results largely support this hypothesis, our conclusions were undermined by data from the Rest condition, which indicated that Mental Workload and Stress were often higher than during tasks. We hypothesize that participants were experiencing anxiety in anticipation of subsequent stress tasks. We discuss this hypothesis and present a revised study designed to better control for this result.

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Workload Alerts—Using Physiological Measures of Mental Workload to Provide Feedback During Tasks

Cited by 34 publications

References 48 publications

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

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

Prefrontal Asymmetry BCI Neurofeedback Datasets

How Stress Affects Functional Near-Infrared Spectroscopy (fNIRS) Measurements of Mental Workload

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