PROJECT PAGE: http://handtracker.mpi-inf.mpg.de/projects/WatchSense/#materials It relies on a depth sensor that tracks movements of the thumb and index finger on and above the back of the hand. In this way, not only can smartwatches be controlled, but also smartphones, smart TVs and devices for augmented and virtual reality. They’re called the “Apple Watch Series 2″, “LG Watch”,…

Release: http://www.aalto.fi/en/current/news/2017-05-03-004/ Computers learn to understand humans better by modelling them 03.05.2017 Computers are able to learn to explain the behavior of individuals by tracking their glances and movements. Researchers from Aalto University, University of Birmingham and University of Oslo present results paving the way for computers to learn psychologically plausible models of individuals simply by…

watchsense

WatchSense uses a depth sensor embedded in a wearable device to expand the input space to neighboring areas of skin and the space above it. Upcoming paper at CHI 2017 by: Srinath Sridhar,  Max Planck Institute for Informatics, Saarbrücken, Germany Anders Markussen , University of Copenhagen, Copenhagen, Denmark Antti Oulasvirta , Aalto University, Helsinki, Finland…

d1_update01

In collaboration with Microsoft Research, we studied the accuracy and precision of eye tracking in practical, everday tracking conditions on over 80 people. We propose design implications for adaptive, error-aware gaze applications, that consider the large variations of eye tracking quality. Upcoming paper at CHI 2017 by: Anna Feit , Aalto University, Helsinki, Finland Shane Williams , Microsoft…

abc4hci

This paper studies advanced inference methods for cognitive modeling in HCI, showing that ABC (i) improves estimates of model parameter values, (ii) enables meaningful comparisons between model variants, and (iii) supports fitting models to individual users. Upcoming paper at CHI 2017 by: Antti Kangasrääsiö , Aalto University, Espoo, Finland Kumaripaba Athukorala , Aalto University, Helsinki,…

teaser

Researchers at Aalto University provide the first analysis of typing strategies. This paper revisits the present understanding of typing, which originates mostly from studies of trained typists using the ten- finger touch typing system. Their goal is to characterise the majority of present-day users who are untrained and employ diverse, self-taught techniques. In a transcription task, they compare…

lee2016modelling2

Researchers at Aalto University present a novel model to predict error rates in temporal pointing. Although temporal pointing is common in interactions requiring temporal precision, rhythm, or synchrony, no previous HCI model predicts error rates as a function of task properties. This model assumes that users have an implicit point of aim but their ability to elicit the…

lee2016spotlights

Researchers at Aalto University contribute a novel technique to facilitate skim reading. In response to motion blur and short object exposure when scrolling large documents, they present Spotlights, a scrolling technique that complements regular continuous scrolling at high speeds (2–20 pages/s). They present a novel design rule informed by theories of the human visual system for dynamically selecting objects and…