Work-in-Progress, PupilWare-M: Cognitive Load Estimation Using Unmodified Smartphone Cameras

Rafiqi, Sohail; Wangwiwattana, Chatchai; Fernández, Ephrem; Nair, Suku; Larson, Eric C.

doi:10.1109/mass.2015.31

Cited by 3 publications

(1 citation statement)

References 15 publications

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“…Although pupil dilation has traditionally been measured with expensive eye-tracking systems, in recent years webcams have rapidly become more capable, to the extent that most current webcams are high definition. Such high-quality webcams can be used to measure pupil dilation changes and calculate workload in normal office conditions (Rafiqi, Wangwiwattana, Fernandez, Nair, & Larson, 2015), promising to make eye tracking and pupil dilation widely available.…”

Section: Discussionmentioning

confidence: 99%

Interrupted by Your Pupil: An Interruption Management System Based on Pupil Dilation

Katidioti

Borst

Haan

et al. 2016

International Journal of Human–Computer Interaction

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Interruptions are prevalent in everyday life and can be very disruptive. An important factor that affects the level of disruptiveness is the timing of the interruption: Interruptions at low-workload moments are known to be less disruptive than interruptions at high-workload moments. In this study, we developed a task-independent interruption management system (IMS) that interrupts users at low-workload moments in order to minimize the disruptiveness of interruptions. The IMS identifies low-workload moments in real time by measuring users' pupil dilation, which is a well-known indicator of workload. Using an experimental setup we showed that the IMS succeeded in finding the optimal moments for interruptions and marginally improved performance. Because our IMS is task-independent-it does not require a task analysis-it can be broadly applied.

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Section: Discussionmentioning

confidence: 99%

Interrupted by Your Pupil: An Interruption Management System Based on Pupil Dilation

Katidioti

Borst

Haan

et al. 2016

International Journal of Human–Computer Interaction

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A System for Noncontact Estimation of Cognitive Load Using Saccadic Parameters Based on a Serio-Parallel Computing Framework

Dasgupta

Bhattacharya²,

Routray

2019

IEEE Trans. Cogn. Dev. Syst.

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PupilNet, Measuring Task Evoked Pupillary Response using Commodity RGB Tablet Cameras

Wangwiwattana

Ding

Larson

2018

Proc. ACM Interact. Mob. Wearable Ubiquitous Technol.

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Pupillary diameter monitoring has been proven successful at objectively measuring cognitive load that might otherwise be unobservable. This paper compares three different algorithms for measuring cognitive load using commodity cameras. We compare the performance of modified starburst algorithm (from previous work) and propose two new algorithms: 2 Level Snakuscules and a convolutional neural network which we call PupilNet. In a user study with eleven participants, our comparisons show PupilNet outperforms other algorithms in measuring pupil dilation, is robust to various lighting conditions, and robust to different eye colors. We show that the difference between PupilNet and a gold standard head-mounted gaze tracker varies only from -2.6% to 2.8%. Finally, we also show that PupilNet gives similar conclusions about cognitive load during a longer duration typing task.

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Work-in-Progress, PupilWare-M: Cognitive Load Estimation Using Unmodified Smartphone Cameras

Cited by 3 publications

References 15 publications

Interrupted by Your Pupil: An Interruption Management System Based on Pupil Dilation

Interrupted by Your Pupil: An Interruption Management System Based on Pupil Dilation

A System for Noncontact Estimation of Cognitive Load Using Saccadic Parameters Based on a Serio-Parallel Computing Framework

PupilNet, Measuring Task Evoked Pupillary Response using Commodity RGB Tablet Cameras

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