You are here

Home » Groups » Eye Data Quality

Project aims

 

The EMRA Technical Committee on eye data quality (see menu left to view subcommittee members) aims to define standard measures of eye data quality in research reports and across systems, and to test these defined methods and measures validity in a series of large scale experiments. Experts from eye tracker design and eye movement research are working together with the wider eye movements community to reach consensus on measures of accuracy and precision, temporal and spatial resolution and the robustness of eye trackers under variant environmental and individual characteristics, and to publish the outcome of tests for the benefit of the community at large. This work is very important to the development of eye tracking and eye movement research for a number of reasons, including:

 
1. We need to know something about the quality of data recorded for every research report using eye movement measures in order to know if the results are comparable to previous research, to replicate results, and to know if the conclusions drawn are valid. There is currently no formal consensus on how to measure and report data quality. Although we do know that details such as sample selection/exclusion criteria, participants, and exact calculations used have large effects, these details are often unknown.
 
2. People interested in buying an eye tracker need to know if it can produce data capable of the measures they need, for the people they wish to study and for the environment they wish to record in, or for the single user who wants to use a gaze controlled interface. Predicint these effects across trackers requires large scale, highly controlled recording of a large number of people on many eyetrackers. 
 
3. Designers of gaze controlled interfaces need to know what range of accuracy and precision they can expect from a system in order to design an interface which will operate smoothly on it. Designers of low cost or highly mobile eyetrackers that are constrained in hardware want to maximise the quality of their data based on empirically driven research, given limited data quality - what measures are possible? How should we opitmally filter or parse the signal? These questions rely on comparison of methods across realistic noise profiles and a realistic range of human variation.
 
4. Manufacturers want to report, compare and optimise the quality of data their systems produce, and know that other manufacturers are using the same measures. Having an independent, research driven body of literature and tools to refer to, or integrate into their own software, help with this. System specifications based on standardised measures are the only realistic way to compare tracker advantages across systems or system modifications.
 
Eye data quality standardisation helps to improve eye tracking in general. Everyone benefits.
Every effort has been made to record data from commercial eyetrackers with set ups that are repeatable in small labs or manufacturer premises. Large scale data recording requires replication of set up at multiple sites - this method and the software to run it are the main output of the work for the development of the field. All software is open source and all interested manufacturers can have support (e.g. template implementations) if they want to run the standard experiments and analyses on their systems.
 
 
 
M T W T F S S
 
 
1
 
2
 
3
 
4
 
5
 
6
 
7
 
8
 
9
 
10
 
11
 
12
 
13
 
14
 
15
 
16
 
17
 
18
 
19
 
20
 
21
 
22
 
23
 
24
 
25
 
26
 
27
 
28
 
29
 
30
 
 
 
 
Participate