Deducing self-interaction in eye movement data using sequential spatial point processes

Penttinen, Antti; Ylitalo, Anna‐Kaisa

doi:10.1016/j.spasta.2016.03.005

Cited by 11 publications

(35 citation statements)

References 19 publications

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“…Algorithm optimization: researchers have proposed using algorithm optimization to improve the accuracy of eye control and smoothen the track of sight movement (Kotani et al, 2012;Lin & Majumder, 2016;Penttinen & Ylitalo, 2016;Velloso et al, 2017).…”

mentioning

confidence: 99%

Improving accuracy of gaze‐control tools: Design recommendations for optimum position, sizes, and spacing of interactive objects

Niu

Zuo

Yang

et al. 2021

Hum Ftrs & Erg Mfg Svc

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To investigate a suitable design of interactive objects in the fixation-triggered eyecontrol interface, this study conducted two ergonomic experiments based on the location of an object, area of the object, and distance between adjacent objects. Experiment 1 investigated the priority distribution of different regions and recommended values of object sizes in different regions. The results showed that middle-middle (MM) area is the optimal area, while middle-left (ML) area, middleright (MR) area, and upper-left (UL) area are suboptimal areas, and the remaining are general areas. The recommended minimum sizes for the optimal area, suboptimal area, and general area are 2.636°(200 px on the screen of a 1920 px × 1080 px 14 inch monitor), 2.978°(225 px on the screen of a 1920 px × 1080 px 14 inch monitor) and 3.322°(250 px on the screen of a 1920 px × 1080 px 14-inch monitor), respectively. Experiment 2 conducted ergonomic experiments of different distances between adjacent objects in horizontal and vertical distributions.The results showed that the appropriate setting of the distribution and size of the objects showed no significant difference between different levels of spacing on the trigger accuracy rate and task completion time. Further, 1.032°(75 px on the screen of a 1920 px × 1080 px 14-inch monitor) could be recommended as the most suitable spacing. According to the conclusions of Experiments 1 and 2, an optimized eye-control interface and the NASA-TLX scale were used to verify the validity and scientificity of the experimental results. The conclusions can be employed to improve the interaction efficiency of the eye-control system and user experience and expand the application field of eye control.

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mentioning

confidence: 99%

Improving accuracy of gaze‐control tools: Design recommendations for optimum position, sizes, and spacing of interactive objects

Niu

Zuo

Yang

et al. 2021

Hum Ftrs & Erg Mfg Svc

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show abstract

“…Points are added until the observed number of points in the pattern has been reached and the main focus here is to make inference on the arrival density. Below, we first recall the general sequential model 28 (Section 3.1) and specify it in our case without (Section 3.2) and with noise (3.4). Further, we discuss efficient inference for the sequential models (Section 3.3) and, finally, fit the sequential model with noise to the sweat gland data (Section 3.5).…”

Section: Sequential Point Process Modelmentioning

confidence: 99%

“…The density function for the whole point pattern ( x 1 , … , x N ) is then

\begin{align} {\overset{boldx}{true}}_{n} \mapsto \frac{1}{false| W false|} true \prod_{k = 2}^{n} f false(x_{k}; {\overset{boldx}{true}}_{k prefix- 1} false), \end{align}

where 1/| W | is the contribution of the first point. A nice feature of the sequential point process models is that they have a tractable likelihood even though it can be costly to compute 28 …”

Section: Sequential Point Process Modelmentioning

confidence: 99%

“…The other model is more physiologically motivated, a generative point process model, where the activated sweat gland pattern is modeled as a thinning of the underlying true (unobserved) sweat gland pattern which is modeled first. While the likelihood of the sequential model is tractable, it has been considered computationally costly to evaluate 28 . Here, we propose an efficient way to perform traditional likelihood‐based inference for a certain type of sequential models, which makes also likelihood‐based Bayesian inference feasible.…”

Section: Introductionmentioning

confidence: 99%

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Point process models for sweat gland activation observed with noise

Kuronen

Myllymäki

Loavenbruck

et al. 2021

Statistics in Medicine

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The aim of this article is to construct spatial models for the activation of sweat glands for healthy subjects and subjects suffering from peripheral neuropathy by using videos of sweating recorded from the subjects. The sweat patterns are regarded as realizations of spatial point processes and two point process models for the sweat gland activation and two methods for inference are proposed. Several image analysis steps are needed to extract the point patterns from the videos and some incorrectly identified sweat gland locations may be present in the data. To take into account the errors, we either include an error term in the point process model or use an estimation procedure that is robust with respect to the errors.

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“…The fixation process could be regarded as a realization of a sequential point process which can be used to access the non-stationarity of the fixation process. The first author of this paper is currently working on that issue (see preprint Penttinen and Ylitalo [2016]).…”

Section: Summary Statisticsmentioning

confidence: 99%

What we look at in paintings: A comparison between experienced and inexperienced art viewers

Ylitalo¹,

Särkkä²,

Guttorp³

2016

Ann. Appl. Stat.

Self Cite

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How do people look at art? Are there any differences between how experienced and inexperienced art viewers look at a painting? We approach these questions by analyzing and modeling eye movement data from a cognitive art research experiment, where the eye movements of twenty test subjects, ten experienced and ten inexperienced art viewers, were recorded while they were looking at paintings.Eye movements consist of stops of the gaze as well as jumps between the stops. Hence, the observed gaze stop locations can be thought as a spatial point pattern, which can be modeled by a spatiotemporal point process. We introduce some statistical tools to analyze the spatio-temporal eye movement data, and compare the eye movements of experienced and inexperienced art viewers. In addition, we develop a stochastic model, which is rather simple but fits quite well to the eye movement data, to further investigate the differences between the two groups through functional summary statistics. ‡

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Deducing self-interaction in eye movement data using sequential spatial point processes

Cited by 11 publications

References 19 publications

Improving accuracy of gaze‐control tools: Design recommendations for optimum position, sizes, and spacing of interactive objects

Improving accuracy of gaze‐control tools: Design recommendations for optimum position, sizes, and spacing of interactive objects

Point process models for sweat gland activation observed with noise

What we look at in paintings: A comparison between experienced and inexperienced art viewers

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