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Posts Tagged ‘eyetracking’

Eyetracking…How Does it Work?

Thursday, October 7th, 2010

Eyetracking has a mystery to it for some reason. Perhaps it’s the perception that scientists can catch glimpses of your thoughts but more than anything it’s the unknown of how the technology works. As cool as seeing into one’s mind is, eyetracking can not record your personal thoughts like in Minority Report (I’m sure someone is  working on it though). Tobii T60_User First Blog

Eyetracking can be defined as a technique that is used to record and measure eye movements. Definition is simple enough, but I always get a follow-up of “how does it record” and “will it hurt”? First let me say no it will not hurt, second you will not go blind, and third you will not become a mutant-sorry. At User First we use the Tobii T60 model, so I will discuss how this equipment works specifically.

Fantastic Machinery, The Eye

Imagine if you will that you are looking out a window from your home or office onto a city street.  As you look outside, your eyes are constantly moving.  Some of these movements are conscious.  For example, you notice the movement of a dog and glance above it for a glimpse of its owner.  But more so your eyes are moving involuntarily, focusing only on certain areas of the visual field in order to form a picture of the scene for your brain. The human eye is a fantastic piece of machinery; it is not capable of absorbing 100% of the visual field in an instant with clarity. We call the area of the eye capable of this focus the foveal area and the brief pauses of our gaze the fixations.PCCR_ User First Blog The foveal area accounts for only 8% of the visual field at any one time but supplies 50% of the visual data received by our brain.  And the movement of the eye controls which regions of the visual field we fixate on and which regions are ignored and left to the poor acuity of our peripheral vision which is only useful for picking up movement and strong contrast.

So how are Eye Movements Tracked?

Just as the human eye relies on the focus and detection of light to see, so does the most common technique used to track eye movements called Pupil Centre Corneal Reflection (PCCR).  This technique is non-intrusive and the technology making it possible comes in two forms: either a specially equipped computer monitor or a head-mounted device.  Both options use a light source to illuminate the eye causing highly visible reflections.  The illumination is near infrared and therefore unnoticeable to the user but creates reflection patterns on the cornea and pupil of the eye and two image sensors on either the computer monitor or the head-mounted device are used to capture images of the eyes and the reflection patterns.  A computer then uses advanced image processing algorithms and a physiological 3D model of the eye to estimate the position of the eye in space and the point of gaze with high accuracy.

The location of these gaze points during each fixation, the time spent on each fixation, and the pattern in movement from one gaze point to another are the key pieces of data collected during an eye tracking study.  These data can then be visualized using a gaze plot or a heatmap.

gaze-plot-blog

CAPTION: The Gaze Plot visualization shows the movement sequence and position of fixations (dots) and saccades (lines) on the observed image or visual scene.

Heatmap-User First blog
CAPTION: The Heatmap visualization highlights the areas of the image where the participants fixated. Warm colors indicate areas where the participants either fixated for a long time or at many occasions.

Not only can we determine what and how visual information is consumed but patterns in eye movement tell us more.  Emotional responses are evident in eye movement patterns and thus allow us to connect physical behavior of the eye to cognitive behavior in the brain.  This is why eyetracking is a strong supplement to traditional qualitative studies.  They allow a scientific measure beyond the subjective responses provided by a participant in an interview.

Eyetracking is especially important in the age of mass media.  The amount of content, the speed at which it is delivered, and the speed at which a user consumes it, means users make less and less time fixated on each image.

How will your message not get lost?  How will your brand be recognized? Question please don’t hesitate to ask.

Mobile eye tracking - part 3 of 3

Tuesday, July 6th, 2010

Here is a brief overview of three approaches to analyzing mobile eye tracking output.

1. Watch the video output and manually tally the ‘hits’. Slowly playback the video and count how often the point of gaze lands on the area of interest. This gives you a general sense of what is looked at and what is ignored, but only limited conclusions can be drawn with respect to the amount of time spent viewing each AOI. One could slow the video down further (frame by frame) and take note of the time stamp for each time the participant starts looking at one particular region. This is enormously tedious, especially for video segments longer than 2 minutes and with more than just a handful of AOI. It is also imprecise; the time stamps are obtained at 30 fps, with up to 5 or more data points lost with each frame, before and after the time stamp. There is also substantial opportunity for human error in tallying hits or recording time (I speak from experience).

cross-hair

2.Identify fixations, then tag AOI based on fixation. In this approach the analysis software identifies fixations following a particular algorithm developed by the manufacturer. Then, thumbnails are generated for the video segment during which the fixation occurred. The research analyst tags each thumbnail with an identification of which AOI is being looked at during that fixation. The output sums the tags and can compute dwell time based on the length of the fixation. This is all very promising and most certainly not as time consuming as option 1. Nonetheless, each data file needs to be addressed individually and to a certain degree, manually. A greater concern, in my opinion, is the reliance on the identification of fixations to then in turn make conclusions about viewing attention and dwell time. Fixations, by definition, assume a moment of movement cessation of the pupil as the eye fixates on an object. What if the object is moving and the pupil is following in smooth pursuit? How is the fixation captured? Experience with this approach has left me wondering why the dwell times on AOIs total up to only a fraction of the total testing session. What did the participant look at the rest of the time? Did he keep moving his eyes so quickly that he never truly looked at anything? Hard to imagine.

3. Draw regions ‘on’ the output videos and process the data against the defined AOI. This is essentially the same idea as most analysis approaches with desktop systems. Identify the AOI in the scene and the software will tally when the x,y coordinates of the point of gaze fall within that region. There are two approaches:

a. Draw each AOI ‘by hand’ for each frame. This is can be reasonable for a small number of AOI and a fairly stable scene. The markers for the AOI can be dragged and re-sized in order to consistently overlay the actual regions in the scene as the video plays. It is potentially more time consuming than option 1, but is about as precise as mobile eye data analysis can get.

7-6-2010-9-28-14-am

b. Use a form of image recognition software and have the software identify AOI. This approach works well if there is some contrast within the scene and you have primarily only one scene to deal. Here you create a video of the scene and identify the AOI for the software. It, in turn, uses this as a key to automatically identify the AOI in the recorded data files. One the AOI are identified, the process is the same as in (a) above. This potentially is a very quick process and results in data that is potentially very precise. It is almost too good to be true.

So that is where we stand at User First with our understanding of mobile tracking. We welcome corrections, other perspectives, elaboration, and the sharing of experiences as it pertains to this topic as we are ourselves learning each day about what is available, what is in the works, and how we might meet client needs in the future.

As we move forward with mobile tracking and expand on our experiences with different analysis methods in particular, we will keep you posted