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Posts Tagged ‘eye tracking’

Heatmap Types in Tobii Studio Explained

Tuesday, October 12th, 2010

A heat map is a graphical representation of data where the values taken by a variable in a two-dimensional map are represented as colors. There are three different types of heat maps in Tobii Studio: Count, Absolute Duration, Relative Duration. If you aren’t sure what the differences are between these fixation data styles, below is a short description along with the visualization of eight participants.

Count

The count is defined as the sum of the number of fixations from all the selected recordings for each section of the image. This plot shows the accumulated number of fixations from all the selected test persons. This means that this plot only shows where people have looked and not for how long. That means that a 2000 ms fixation will look the same as a 100 ms fixation.

count-image-ufi-blog

Absolute Duration

This plot shows how long the selected test persons have looked at the different areas in the image. This means a 2000 ms fixation will be 20 times higher (in color value) than a 100 ms fixation. It also means that a person that has spent a long time looking at a stimulus will have a greater impact on the heat map than someone who only glanced at it shortly.

absolute-duration-image-ufi-post

Relative Duration

The sum of the individual fixation lengths relative to the total fixation time on the image for each recording, if in recording A, the subject looked at the image for 10 seconds and at a specific object for 3 seconds and in recording B, the subject looked at the image for 10 minutes and at the specific object for 3 minutes. In this kind of Heat Map the data from recording A and recording B will have the same weight. Naturally, what is considered a fixation is dependent on the settings chosen in Fixation Filters.

Relative-Duration Image-UFI Post

All three fixation styles are valuable and are dependent on the design of the research study,  but if you have any questions on which one you should just let me know!

UPDATE: Jon Ward from Acuity added a very useful comment on when you should use the different styles of heatmaps. Check the comment below!

Mobile eye tracking - part 2 of 3

Monday, May 31st, 2010

Challenge 1: Make the eye tracker small enough to carry about, secure enough to prevent shifting with movement and discrete enough to not make a scene.

Desktop eye trackers include a monitor, a series of cameras trained on the eyes that either stand alone or are built into the monitor, and a recording unit of some kind, both for a steady stream of eye data and video capture of the monitor screen. Mobile trackers include the same, but now a monitor is not needed; instead, there is an additional camera unit, the scene camera. This camera records the scene as the participant encounters it and needs to be ‘attached’ to the user close to same plane and position as the eyes. This is potentially a lot of equipment that now needs to be carried around by the participant.

SR EyeLink w/ scene camera

SR EyeLink w/ scene camera

One of the earlier mobile tracking systems that became available and was reasonably accurate was a modification of the desktop eye tracking system. SR Research took their EyeLink system and added a scene camera. This worked because EyeLink was not a remote system; it was a head-mounted unit and had room along the headband to host a scene camera. This head-mounted unit did not include the recording system and thus was tethered to a processor and hard drive. The cable was rather thick (thickness of a finger) and was limited to 40 feet. The recording system could be placed on a cart and with a long extension cord, could be pushed around after the participant. This system was certainly secure enough, but not designed for mobility or discretion. It was effective for small spaces, such as flipping through magazines, considering a display stand, or evaluating a single shelf set or package in hand. Nonetheless, the headgear was rather cumbersome and definitely drew attention.

Courtesy SMI

Courtesy SMI

Luckily in the last 1-2 years there have been tremendous developments. Scene and eye cameras are significantly smaller and lighter and can be attached either to a cap or to a pair of glasses. Mobile trackers are still tethered; wireless systems are in the works, but so far the data streams are too heavy (with up to 100 data points per second and video from 2 cameras at 30 frames per second). Nonetheless, the cables are small, not much larger than those to your ear buds on your iPod. And, more importantly, they are tethered to equipment that is substantially less bulky - usually a recording device less than half the size of your typical laptop. This can be easily carried in a pouch that hangs over the shoulder of the participant or is otherwise attached.

Courtesy ASL

Courtesy ASL

Which approach is more effective - glasses or wearing a cap? Glasses have certain appeal because they are smaller and less noticeable. With proper straps these glasses can be secured so vigorous head movement does not shift the cameras about. Camera movement can result in a significant and undesirable shift in the calibration (i.e. what the data or video indicates the user is looking at is no longer what the user is really looking at). Glasses are more easy to secure than a baseball cap.

But glasses pose certain problems. They cannot be used if the participant wears prescription glasses (believe me, we’ve tried!). Further, the positioning of the eye camera and cut of the glasses is designed for a certain face structure. Deviate from this standard and the edge of the opening cut into the lens falls between the camera and the eye, distorting the camera’s view of the pupil.

Wearing a baseball cap with cameras attached offers solutions for both of these

Courtesy SMI

Courtesy SMI

challenges. The camera units attached to a cap are more flexible and offer more options for adjustment, allowing for accurate tracking of virtually any type of participant, young and old, with or without glasses, and any nationality. What about the camera shift? We’ve been reassured by the manufacturer that camera shift is monitored and seamlessly corrected via the tracking of the corneal reflection. If this is indeed the case, we’re sold! We have the opportunity to test out such a system in the coming weeks.

There are different mobile trackers available, and they differ not only in the hardware; some use dark pupil, some light, some with or without corneal reflection. Steps to calibrate, record and monitor in real-time varies by manufacturer. The robustness of the systems, especially if tracking in daylight or in particularly ‘bumpy’ environments (such as road car rallies!) varies. Detailed discussion of this will be dealt with in another post. For now, let me just say that not all mobile trackers are the same and do need to be carefully evaluated.

Mobile eye tracking - part 1 of 3

Tuesday, May 18th, 2010

For many years eye tracking has been limited to a controlled, virtual environment, making precise data collection and analysis relatively simple:

  • the computer screen doesn’t move,
  • participant movement is limited,
  • and stimulus presentations on screen are generally consistent across participants.

The biggest challenge researchers face is correcting for head movement (turning sideways or leaning forward) and managing point of gaze data on sometimes unexpected dynamic stimuli (pop-ups, animated ads, video, scrolling, etc.).

Nonetheless, it was relatively simple to track samples of 100 or more and evaluate their viewing pattern as they looked at on-screen presentations. Dynamic backgrounds posed a challenge, but as long as all participants looked at the same stimulus, it wasn’t too big of a deal to analyze this. It was time consuming to identify the areas of interest (AOI) for a background that is constantly changing, but once they are identified, everyone’s data can be run against the same AOIs (as identified, for example, in a television commercial). The output is then the same as for static backgrounds: precise dwell time and fixation information for areas of interest that can be aggregated across all participants. And this can easily be plotted on a static image of the background for visualization.

This type of testing was great for websites, TV commercials and software usability testing to name a few, but was less realistic when evaluating stimuli such as shelf displays, package designs, magazines or products in hand. This posed a problem; a golden rule in user research is to test real users in real environments. Eye tracking participants looking at virtual shelf displays on a computer screen just isn’t the same as eye tracking them as they look at actual packages on a shelf (though this certainly is up for debate).

Mobile eye tracker - image courtesy http://www.mangold-international.comThus came the shift to mobile eye tracking - recording a person’s point of gaze as he or she is moving about in a real, 3-D environment. This could be selecting magazines off of a shelf and reading them, moving about a store selecting products off of a shelf, or interacting with signage at a baseball game. The same technology could apply and has been modified for this type of testing, but there have been a number of obstacles along the way involving either the hardware or the software:

  1. Hardware: While hardware has been reduced in scope and size, a fast processor with a big enough hard drive is still needed, as are at least two cameras (one for the eye, one for the scene). The connection between computer and cameras needs to be wireless or all be so lightweight that it’s easily portable. The cameras have to be secured to the head in a way to limit any shift between camera and eye. How to manage these limitations effectively?

  1. Software: The calibration process and recording the data is much the same. The challenge is in the follow-up analysis. How do you identify regions and analyze point of gaze data when the background is constantly changing and is unique for each and every participant? How can you identify AOI rapidly and accurately with such variability? How can we aggregate data across participants when the stimuli varies so significantly? Can it be done to match the stable scene analysis that we are accustomed to, or is it necessary to make a significant paradigm shift in how we approach mobile eye data?

Different manufacturers and engineers have approached and managed these obstacles in unique ways. With the next few blogs I plan to explore that a bit further. I can only provide information based on personal research and experience, and am eager to hear more about experiences others have had. It’s an exciting new technology; although it has been around for years, there have been and continue to be substantial developments that bring this research approach more to the forefront.