Paper Info
Title
Accurate measurements of pointing performance from in situ observations
Abstract
We present a method for obtaining lab-quality measurements of pointing performance from unobtrusive observations of natural in situ interactions. Specifically, we have developed a set of user-independent classifiers for discriminating between deliberate, targeted mouse pointer movements and those movements that were affected by any extraneous factors. To develop and validate these classifiers, we developed logging software to unobtrusively record pointer trajectories as participants naturally interacted with their computers over the course of several weeks. Each participant also performed a set of pointing tasks in a formal study set-up. For each movement, we computed a set of measures capturing nuances of the trajectory and the speed, acceleration, and jerk profiles. Treating the observations from the formal study as positive examples of deliberate, targeted movements and the in situ observations as unlabeled data with an unknown mix of deliberate and distracted interactions, we used a recent advance in machine learning to develop the classifiers. Our results show that, on four distinct metrics, the data collected in-situ and filtered with our classifiers closely matches the results obtained from the formal experiment.
Year
DOI
Venue
2012
10.1145/2207676.2208733
CHI
Keywords
Field
DocType
formal study set-up,formal experiment,accurate measurement,situ observation,record pointer trajectory,distinct metrics,formal study,targeted movement,unlabeled data,targeted mouse pointer movement,situ interaction,data collection,machine learning
Pointer (computer programming),Computer vision,Computer science,Jerk,Pointer (user interface),Human–computer interaction,Software,Acceleration,Artificial intelligence,Machine learning,Trajectory
Conference
Citations 
PageRank 
References 
10
0.53
17
Authors
3
Name
Order
Citations
PageRank
Krzysztof Z. Gajos11837127.94
Katharina Reinecke249740.37
Charles Herrmann3122.25