Exploring workload and attention measurements with uLog mouse data

Drunen, Annemiek van; Broek, Egon L. van den; Spink, Andrew; Heffelaar, Tobias

doi:10.3758/brm.41.3.868

Cited by 12 publications

(11 citation statements)

References 26 publications

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“…Bailenson et al [25] combined computer vision and physiological measures. Van Drunen et al [682] combined physiological measures with eye tracking, thinking aloud, and user-system interactions (cf. [680]).…”

Section: Ubiquitous Signals Of Emotionmentioning

confidence: 99%

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Affective signal processing (ASP) : unraveling the mystery of emotions

Broek¹

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Section: Ubiquitous Signals Of Emotionmentioning

confidence: 99%

“…However, most evidence is provided for the HR variability (HRV) [304,349]. HRV decreases with an increase in mental effort, stress, and frustration [85,682]. Moreover, HRV can be influenced by the valence of an event, object, or action [14,481,538].…”

Section: Ubiquitous Signals Of Emotionmentioning

confidence: 99%

Affective signal processing (ASP) : unraveling the mystery of emotions

Broek¹

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“…These data provide insights about the usability of the evaluated product (Nielsen, 1993). Van Drunen, van den Broek, Spink, and Heffelaar (2009), for example, recorded user actions as an indication of user workload while performing a Web-based task. However, the recorded mouse actions were not used in the analyses of the eye movements or screen captures (videos) recorded during the experiment, but rather the number of mouse movements were compared with the number of fixations (and other measurements).…”

Section: Eye Tracking and Dynamic Stimuli: Existing Solutionsmentioning

confidence: 99%

Combining user logging with eye tracking for interactive and dynamic applications

et al. 2014

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User evaluations of interactive and dynamic applications face various challenges related to the active nature of these displays. For example, users can often zoom and pan on digital products, and these interactions cause changes in the extent and/or level of detail of the stimulus. Therefore, in eye tracking studies, when a user's gaze is at a particular screen position (gaze position) over a period of time, the information contained in this particular position may have changed. Such digital activities are commonplace in modern life, yet it has been difficult to automatically compare the changing information at the viewed position, especially across many participants. Existing solutions typically involve tedious and time-consuming manual work. In this article, we propose a methodology that can overcome this problem. By combining eye tracking with user logging (mouse and keyboard actions) with cartographic products, we are able to accurately reference screen coordinates to geographic coordinates. This referencing approach allows researchers to know which geographic object (location or attribute) corresponds to the gaze coordinates at all times. We tested the proposed approach through two case studies, and discuss the advantages and disadvantages of the applied methodology. Furthermore, the applicability of the proposed approach is discussed with respect to other fields of research that use eye tracking-namely, marketing, sports and movement sciences, and experimental psychology. From these case studies and discussions, we conclude that combining eye tracking and user-logging data is an essential step forward in efficiently studying user behavior with interactive and static stimuli in multiple research fields. Keywords Eye tracking . Interactivity . GeoreferencingEye tracking has proven to be a helpful technique in user research, especially when a visual element needs to be evaluated. By using eye tracking data, researchers can discover how long and how often a user looks at a particular area of interest, as well as the length and speed of the eye movements (Duchowski, 2007;Holmqvist et al., 2011). The position of the gaze (also termed the point of regard, or POR) is typically expressed using screen coordinates in pixels. From these basic screen coordinate measurements, various gaze metrics are derived in relation to what is displayed, such as the fixation duration (how long), fixation count (how often), and various scan-path characteristics (e.g., the length and speed of eye movements). The technique has been applied in a multitude of research fields, including software engineering, industrial engineering (e.g

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“…Bailenson et al [5] combined computer vision and physiological measures. Van Drunen et al [100] combined physiological measures with eye tracking, thinking aloud, and user-system interactions (cf.…”

Section: Ubiquitous Signals Of Emotionmentioning

confidence: 99%

“…However, most evidence is provided for the HR variability (HRV) [40]. HRV decreases with an increase in mental effort, stress, and frustration [15,100]. Moreover, HRV can be influenced by the valence of an event, object, or action [3,71,81].…”

Section: Ubiquitous Signals Of Emotionmentioning

confidence: 99%

Ubiquitous emotion-aware computing

Broek

2011

Pers Ubiquit Comput

Self Cite

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Emotions are a crucial element for personal and ubiquitous computing. What to sense and how to sense it, however, remain a challenge. This study explores the rare combination of speech, electrocardiogram, and a revised Self-Assessment Mannequin to assess people's emotions. 40 people watched 30 International Affective Picture System pictures in either an office or a living-room environment. Additionally, their personality traits neuroticism and extroversion and demographic information (i.e., gender, nationality, and level of education) were recorded. The resulting data were analyzed using both basic emotion categories and the valence-arousal model, which enabled a comparison between both representations. The combination of heart rate variability and three speech measures (i.e., variability of the fundamental frequency of pitch (F0), intensity, and energy) explained 90% (p \ .001) of the participants' experienced valence-arousal, with 88% for valence and 99% for arousal (ps \ .001). The six basic emotions could also be discriminated (p \ .001), although the explained variance was much lower: 18-20%. Environment (or context), the personality trait neuroticism, and gender proved to be useful when a nuanced assessment of people's emotions was needed. Taken together, this study provides a significant leap toward robust, generic, and ubiquitous emotion-aware computing.

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Exploring workload and attention measurements with uLog mouse data

Cited by 12 publications

References 26 publications

Affective signal processing (ASP) : unraveling the mystery of emotions

Affective signal processing (ASP) : unraveling the mystery of emotions

Combining user logging with eye tracking for interactive and dynamic applications

Ubiquitous emotion-aware computing

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