We present WaveTrace, a novel interaction technique based on selection by motion matching. In motion matching systems, targets move continuously in a singular and pre-defined path – users interact with these by performing a synchronous bodily movement that matches the movement of one of the targets (see image). Unlike previous work which tracks user input through optical systems, WaveTrace is arguably the first motion matching technique to rely on motion data from inertial measurement units readily available in many wristworn wearable devices such as smart watches. To evaluate the technique, we conducted a user study in which we varied: hand; degrees of visual angle; target speed; and number of concurrent targets. Preliminary results indicate that the technique supports up to eight concurrent targets; and that participants could select targets moving at speeds between 180 and 270°/s (mean acquisition time of 2237ms, and average success rate of 91%).
The paper WaveTrace: Motion Matching Input using Wrist-Worn Motion Sensors is accepted for publication at the ACM CHI Conference on Human Factors in Computing Systems (CHI ’17) in Denver, Colorado, May 6-11. The paper was co-written by Javed Khan, Saskia Bakker and Augusto Esteves (Edinburgh Napier University) as part of my Master Thesis.