mobile eyetracking voor_uxd_testing

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Mobiele eye-tracking voor UXD testing Geert Brône & Toon Goedemé, Ph.D.’s van Katholieke Universiteit Leuven

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Page 1: Mobile eyetracking voor_uxd_testing

Mobiele eye-tracking voor UXD testing

Geert Brône & Toon Goedemé, Ph.D.’s van Katholieke Universiteit Leuven

Page 2: Mobile eyetracking voor_uxd_testing

Mobile eye-tracking for UX testing

Geert Brône & Toon Goedemé

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Mobile eye-tracking

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Mobile eye-tracking

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mobile

eye-tracking

user

in the wild

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Mobile eye-tracking

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In the wild?

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In the wild!

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Taking mobile eye-tracking into the wild

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A brief history of eye-tracking research

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Long-standing interest in the study of visual attention in various research disciplines:

Psycholinguistics: reading research

Psychology: scene perception

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A brief history of eye-tracking research

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A more recent research interest can be observed in:

Human-computer

interaction Human-human interaction

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A brief history of eye-tracking research

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Navigation & wayfinding

Kinematics & sports research

A more recent research interest can be observed in:

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Developments in eye-tracking technology

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Broadening interest & technological innovation go hand in hand

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Developments in eye-tracking technology

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•  First generation eye-trackers

•  Unobtrusive eye-trackers

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Developments in eye-tracking technology

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•  Mobile eye-trackers

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Developments in eye-tracking technology

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User-friendly & flexible recording devices are one thing, but efficient data analysis is a completely different story

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Developments in eye-tracking technology

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Recent development: Efficient data aggregation and analysis tools: semi-automatic analysis & results presentation à towards plug and play systems

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Developments in eye-tracking technology

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Ok for static recording devices such as screen-based eye-trackers

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Developments in eye-tracking technology

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Not ok for mobile eye-trackers

Ø No fixed reference frame (moving head, moving subject)

Ø Potentially multiple moving objects in the scene

Ø Highly complex datastream for mobile eye-tracking

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Developments in eye-tracking technology

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•  Manual coding o  time-consuming (and thus expensive!) o  requires technical expertise

Current options:

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Developments in eye-tracking technology

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•  Predefine potentional area of analysis o  Based on infrared (or other) markers o  2-D plane of zone predefined by markers o  Semi automatic data aggregation & analysis possible

Current options:

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Developments in eye-tracking technology

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•  Predefine potential area of analysis

Current options:

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•  But: o  Works only for predefined planes o  Tracking multiple fields or objects with identical or similar features

(object categories) o  Objects of interest need to be tied to a fixed position in the AOA

(<-> handling of objects) o  Labo setup / large natural test environments

Developments in eye-tracking technology

Eye-tracking in the wild?

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Introducting the InSight Out method

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•  Apply image processing techniques on data collected by a mobile eye-tracker

•  (Semi)-Automatic analysis of (mobile) eye-tracking data without predefined AOA’s

•  User-friendly output generation: o  Time-line o  Statistical data o  Object clouds o  …

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•  Integration of image recognition algorithms •  Benefits:

o  Target of analysis is not restricted to a region o  Objects can be moving o  Manual labour limited

Introducting the InSight Out method

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Basic image recognition techniques

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Technique #1: Object recognition based on local feature matching

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Object recognition in eye tracking video

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User’s selection

Visual similarity

score

[ORB: an efficient alternative to SIFT and SURF, E. Rublee & G. Bradski, ICCV 2011 ]

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Realizations Object recognition - GUI

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Object recognition results

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Processing  speed  

Recognition  rate  

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Basic image recognition techniques

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Technique #2: Varying shape detection -> e.g. people, faces

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Varying shape detection

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•  Detection of persons: we trained a new model to detect upper part of a human body (upper 60% of full body) o  [Parts-based latent SVM cascaded classifier, P. Felzenszwalb, CVPR 2010 ]

•  Face detection: 3 face models (frontal, left and right profile) o  [Viola&Jones: Robust Real-time Object Detection, IJCV 2001]

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Eye-tracker experiments Experiments used for development and testing

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•  Visiting a library and picking up magazines •  Walking through a public building while paying

attention to signs such as fire exit, staircases

•  Walking through the streets while paying attention to traffic signs

•  Visiting a toy shop and picking up products

•  Attending a presentation given by a lecturer

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Eye-tracker experiments Case study 1: customer journey experiment

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•  “Gain insights in the experience of customers” •  Find relation between user experience and visual behavior •  Experiment was performed in Museum M (Leuven) •  Visiting a specific exhibition: Hieronymus Cock •  14 participants were involved in this experiments •  4 systems were used

o  Tobii / arrington / contour head mounted camera

•  We collected 160GB of data

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Eye-tracker experiments Case study 1: customer journey experiment

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•  Questions to be answered: •  Do the visitors notice to walking guides? •  Do the visitor notice the childquiz? •  Do the visitors notice the Ipod / Ipad in the exhibition •  Is there a relation between favorite work and view time?

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Eye-tracker experiments Case study 1: customer journey experiment

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•  First result of our algorithm

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Future developments: near future

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Attractive visualisations of the detection results

Detection results database

•  Timeline

•  Object statistics

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•  Camera-based localisation and 3D mapping o  Test person location: 2D heat map and location tracks o  3D gaze location: 3D-heat map

•  Possibly combined with detected objects

Future developments: further future

Obj1  

Obj2  

Obj3  

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•  Emotion recognition o  Important aspect of customer journey analysis in UX o  mobile eye-tracker with additional camera which captures the face o  allows to use existing emotion detection algorithms based on the

pose of e.g. mouth corners o  Link with detection of touch points and visualize…

Future developments: quite far future

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Project planning

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2 parallel tracks •  PhD research on more theoretical aspects

o  Stijn De Beugher o  Sept 2012 - 2016

•  Commercial valorisation o  Working towards spin-off startup o  Mission: processing eye-tracking data from experiments conducted

by UX/marketing research bureaus o  Result: nice-looking reports o  Accepting first commercial projects by Q1 2014

guinea pig discount for first

projects!

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Questions?

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Contact details: www.eavise.be/insightout [email protected] [email protected]