Recognition of Human’s Implicit Intention Based on an Eyeball Movement Pattern Analysis

Jang, Young-Min; S, Lee; Mallipeddi, Rammohan; Kwak, Ho-Wan

doi:10.1007/978-3-642-24955-6_17

Cited by 9 publications

(10 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Furthermore, intention recognition is widely used in Web applications (Chen et al, 2002). In this study, we redefine and present following classification (Jang, Lee, Mallipeddi, Kwak, & Lee, 2011) (b) Informational search intent: This refers to a human's aspiration to find a particular object of interest, or to behave with motivation.…”

Section: Human Intentionmentioning

confidence: 99%

“…The gaze point helps in determining the face of interest in the scene. Pupil dilation helps to determine if the user is interested to know about the face, or trying to remember the identity and details of the person in question (Jang et al, 2011). Intention reasoning attempts to extract specific information according to implicit or explicit user intention.…”

Section: Overall System Architecturementioning

confidence: 99%

“…A user's selective attention point can be calculated by gaze point detection using the pupil centre position (Jang et al, 2011). There are two general types of eye movement detection techniques: bright pupil and dark pupil.…”

Section: Users' Selective Attentionmentioning

confidence: 99%

See 2 more Smart Citations

Active glass-type human augmented cognition system considering attention and intention

Kim

Ojha

2015

Connection Science

Self Cite

View full text Add to dashboard Cite

Human cognition is the result of an interaction of several complex cognitive processes with limited capabilities. Therefore, the primary objective of human cognitive augmentation is to assist and expand these limited human cognitive capabilities independently or together. In this study, we propose a glass-type human augmented cognition system, which attempts to actively assist human memory functions by providing relevant, necessary and intended information by constantly assessing intention of the user. To achieve this, we exploit selective attention and intention processes. Although the system can be used in various real-life scenarios, we test the performance of the system in a person identity scenario. To detect the intended face, the system analyses the gaze points and change in pupil size to determine the intention of the user. An assessment of the gaze points and change in pupil size together indicates that the user intends to know the identity and information about the person in question. Then, the system retrieves several clues through speech recognition system and retrieves relevant information about the face, which is finally displayed through head-mounted display. We present the performance of several components of the system. Our results show that the active and relevant assistance based on users' intention significantly helps the enhancement of memory functions.

show abstract

Section: Human Intentionmentioning

confidence: 99%

Section: Overall System Architecturementioning

confidence: 99%

See 1 more Smart Citation

Active glass-type human augmented cognition system considering attention and intention

Kim

Ojha

2015

Connection Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Steichen et al analysed eye gaze patterns in interactive visualisation tasks using a number of classification methods [20].Their results showed that using simple machine learning on eye tracking metrics can infer a number of task and user characteristics. A number of studies demonstrated the possibility of using eye movement features to classify mental states in scene viewing tasks [14,12,16]. Kardan et al recorded eye movements from 72 participants while performing three tasks: visual search, scene memorization, and aesthetic preference [16].…”

Section: Decoding Mental and Cognitive Statesmentioning

confidence: 99%

“…Their results showed that eye movement distributional properties can classify mental states both within and across individuals. Eye movements are also found to be signatures of implicit navigational and information search intention [14], interaction intents in command issuing [2].…”

Section: Decoding Mental and Cognitive Statesmentioning

confidence: 99%

Monitoring Dementia with Automatic Eye Movements Analysis

Zhang

Wilcockson

Kim

et al. 2016

Intelligent Decision Technologies 2016

View full text Add to dashboard Cite

Abstract. Eye movement patterns are found to reveal human cognitive and mental states that can not be easily measured by other biological signals. With the rapid development of eye tracking technologies, there are growing interests in analysing gaze data to infer information about people' cognitive states, tasks and activities performed in naturalistic environments. In this paper, we investigate the link between eye movements and cognitive function. We conducted experiments to record subject's eye movements during video watching. By using computational methods, we identified eye movement features that are correlated to people's cognitive health measures obtained through the standard cognitive tests. Our results show that it is possible to infer people's cognitive function by analysing natural gaze behaviour. This work contributes an initial understanding of monitoring cognitive deterioration and dementia with automatic eye movement analysis.

show abstract

Probing of human implicit intent based on eye movement and pupillary analysis for augmented cognition

Hwang

Jang

Mallipeddi

2013

Int J Imaging Syst Tech

Self Cite

View full text Add to dashboard Cite

The ultimate purpose of augmented cognition is to enhance human cognitive abilities, which are intrinsically limited. To enhance limited human cognitive abilities, we developed a human augmented cognition system that can offer appropriate information or services by actively responding to the user's intention. This article mainly describes a framework for probing human implicit intentions for the purpose of augmented cognition. The type of user intention, either task-free human implicit intention or task-oriented human implicit intention, can be predicted based on fixation count, fixation length, and pupil size variation induced by eye response. Further, these features are used to detect the transition point between taskfree human implicit intention and task-oriented human implicit intention. Maximum a Posteriori in Naïve Bayes classification model is used for selecting relevant query keywords to search and retrieve specific information from a personalized knowledge database. The experimental results show that the proposed human intention recognition and probing models are suitable for achieving the goal of augmented cognition.

show abstract

Recognition of Human’s Implicit Intention Based on an Eyeball Movement Pattern Analysis

Cited by 9 publications

References 8 publications

Active glass-type human augmented cognition system considering attention and intention

Active glass-type human augmented cognition system considering attention and intention

Monitoring Dementia with Automatic Eye Movements Analysis

Probing of human implicit intent based on eye movement and pupillary analysis for augmented cognition

Contact Info

Product

Resources

About