2018
DOI: 10.3390/s18020458
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Using Psychophysiological Sensors to Assess Mental Workload During Web Browsing

Abstract: Knowledge of the mental workload induced by a Web page is essential for improving users' browsing experience. However, continuously assessing the mental workload during a browsing task is challenging. To address this issue, this paper leverages the correlation between stimuli and physiological responses, which are measured with high-frequency, non-invasive psychophysiological sensors during very short span windows. An experiment was conducted to identify levels of mental workload through the analysis of pupil … Show more

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Cited by 71 publications
(41 citation statements)
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References 81 publications
(101 reference statements)
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“…We also compared our method to existing approaches for inferring relevance using eye-movements, where the data is collapsed into a set of handcrafted features (discussed in Section 2.1). Perceived-relevance of documents are predicted from these features using popular classifiers like Random Forests [34,64] and Support Vector Machines (SVM) [38,52]. We computed 20 such hand-engineered features, aggregated at the user-doc level, and classified them using Random Forest and SVM.…”
Section: Comparison To Existing Standardmentioning
confidence: 99%
“…We also compared our method to existing approaches for inferring relevance using eye-movements, where the data is collapsed into a set of handcrafted features (discussed in Section 2.1). Perceived-relevance of documents are predicted from these features using popular classifiers like Random Forests [34,64] and Support Vector Machines (SVM) [38,52]. We computed 20 such hand-engineered features, aggregated at the user-doc level, and classified them using Random Forest and SVM.…”
Section: Comparison To Existing Standardmentioning
confidence: 99%
“…Moreover, workload measurement affects both the design and management of interfaces. On the one hand, by testing the workload of subjects during the use of web interfaces [9], for example, it is possible to direct the design. On the other hand, in the field of adaptive automation, it is the continuous monitoring of the workload level of the subject that allows the system to vary the feedback in response to the mental state of the operator [10], [11].…”
Section: Multifaceted Aspects Of Workloadmentioning
confidence: 99%
“…Taking into account the nature of the features, there are countless different examples of configurations, in terms of number of channels and frequencies used in the literature. The number of electrodes can vary from 64 [24], [36] to 6 [37], and even the bands considered vary from 2 (Theta and Alpha, [9]), to 7 (0-4 Hz, 4-7 Hz, 7-12 Hz, 12-30 Hz, 30-42 Hz, 42-84 Hz, 84-128 Hz [38]), up to considering all the single frequency bins that define the spectrum [39]. Several studies have shown that it does not necessarily take more than 5-10 electrodes to classify the workload [24].…”
Section: Machine Learning To Get Back Out-of-the-labmentioning
confidence: 99%
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