Voluntary Eye Movements in Human—Computer Interaction

Surakka, Veikko; Illi, Marko; Isokoski, Poika

doi:10.1016/b978-044451020-4/50026-8

Cited by 12 publications

(4 citation statements)

References 12 publications

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“…If full facial muscle activity is preserved the basic idea of the new technique is easily adapted to much more rich means of communication and environmental control. The corrugator supercilii muscle site was chosen for the present study because it is located near the eyes, and was considered to be appropriate to be used in conjunction with the gaze direction [see also Surakka et al 2003]. In addition, there is evidence that increased frowning activity is naturally related to the tasks that require more cognitive processing [van Boxtel and Jessurum 1993;Hietanen et al 1998].…”

Section: Discussionmentioning

confidence: 99%

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Gazing and frowning as a new human--computer interaction technique

Surakka

Illi

Isokoski

2004

ACM Trans. Appl. Percept.

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The present aim was to study a new technique for human-computer interaction. It combined the use of two modalities, voluntary gaze direction and voluntary facial muscle activation for object pointing and selection. Fourteen subjects performed a series of pointing tasks with the new technique and with a mouse. At short distances the mouse was significantly faster than the new technique. However, there were no statistically significant differences at medium and long distances between the techniques. Fitts' law analyses were performed both by using only error-free trials and using also data including error trials (i.e., effective target width). In all cases both techniques seemed to follow Fitts' law, although for the new technique the effective target width correlation coefficient was smaller R = 0.776 than for the mouse R = 0.991. The regression slopes suggested that at very long distances (i.e., beyond 800 pixels) the new technique might be faster than the mouse. The new technique showed promising results already after a short practice and in the future it could be useful especially for physically challenged persons.

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Section: Discussionmentioning

confidence: 99%

“…The idea of combining voluntarily directed eye movements (i.e., voluntarily controlled fixations and gaze direction) and voluntarily produced changes in the level of electrical activity of facial muscles as a new HCI technique has recently been studied and theorized Surakka et al 2003]. There are several reasons for these developments.…”

Section: Introductionmentioning

confidence: 99%

Gazing and frowning as a new human--computer interaction technique

Surakka

Illi

Isokoski

2004

ACM Trans. Appl. Percept.

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“…In such applications, effective modelling of human behaviours and cognition is essential to build adaptation and personalisation mechanisms. Interaction logs are generally not adequate for genuinely interpreting human behaviours; tasks such as problem solving and reading are hard to be assessed based on verbal protocols [17,5]. In addition, the more information exchanged between the user and the system through multiple modalities, the more versatile, efficient and natural the interaction becomes [17].…”

Section: Introductionmentioning

confidence: 99%

Inferring Human Knowledgeability from Eye Gaze in Mobile Learning Environments

Çeliktutan

Demiris

2019

Lecture Notes in Computer Science

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What people look at during a visual task reflects an interplay between ocular motor functions and cognitive processes. In this paper, we study the links between eye gaze and cognitive states to investigate whether eye gaze reveal information about an individual's knowledgeability. We focus on a mobile learning scenario where a user and a virtual agent play a quiz game using a hand-held mobile device. To the best of our knowledge, this is the first attempt to predict user's knowledgeability from eye gaze using a noninvasive eye tracking method on mobile devices: we perform gaze estimation using front-facing camera of mobile devices in contrast to using specialised eye tracking devices. First, we define a set of eye movement features that are discriminative for inferring user's knowledgeability. Next, we train a model to predict users' knowledgeability in the course of responding to a question. We obtain a classification performance of 59.1% achieving human performance, using eye movement features only, which has implications for (1) adapting behaviours of the virtual agent to user's needs (e.g., virtual agent can give hints); (2) personalising quiz questions to the user's perceived knowledgeability.

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“…We attempted to infer aspects of a user's mental state using eye gaze, which has been shown to be useful in training contexts [31]. Interaction logs and verbal protocols are generally not adequate for genuine cognitive and social profiling [82]. However, nonverbal cues such as eye gaze have been frequently studied for inferring user internal states in this context, as eye movements directly reflect what is at the centre of an individual's visual attention, and are linked to cognitive processes in the mind [85].…”

Section: Eye-gaze Trackingmentioning

confidence: 99%

A Cloud-based Robot System for Long-term Interaction: Principles, Implementation, Lessons Learned

Kaptein

Kiefer²,

Cully

et al. 2021

J. Hum.-Robot Interact.

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Making the transition to long-term interaction with social-robot systems has been identified as one of the main challenges in human-robot interaction. This article identifies four design principles to address this challenge and applies them in a real-world implementation: cloud-based robot control, a modular design, one common knowledge base for all applications, and hybrid artificial intelligence for decision making and reasoning. The control architecture for this robot includes a common Knowledge-base (ontologies), Data-base, “Hybrid Artificial Brain” (dialogue manager, action selection and explainable AI), Activities Centre (Timeline, Quiz, Break and Sort, Memory, Tip of the Day, \ldots ), Embodied Conversational Agent (ECA, i.e., robot and avatar), and Dashboards (for authoring and monitoring the interaction). Further, the ECA is integrated with an expandable set of (mobile) health applications. The resulting system is a Personal Assistant for a healthy Lifestyle (PAL), which supports diabetic children with self-management and educates them on health-related issues (48 children, aged 6–14, recruited via hospitals in the Netherlands and in Italy). It is capable of autonomous interaction “in the wild” for prolonged periods of time without the need for a “Wizard-of-Oz” (up until 6 months online). PAL is an exemplary system that provides personalised, stable and diverse, long-term human-robot interaction.

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Voluntary Eye Movements in Human—Computer Interaction

Cited by 12 publications

References 12 publications

Gazing and frowning as a new human--computer interaction technique

Gazing and frowning as a new human--computer interaction technique

Inferring Human Knowledgeability from Eye Gaze in Mobile Learning Environments

A Cloud-based Robot System for Long-term Interaction: Principles, Implementation, Lessons Learned

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