This study explored interactions between users and a 3D virtual world using a multi-touch display and sought to identify the advantages of this form of interaction. An experiment was conducted with 28 subjects, who were classified into two groups: experienced gamers and casual gamers. The experiment involved a pseudo-universe, where users were required to find five objects using the multi-touch display, and five objects using a game controller (GamePad). Overall, multi-touch was slightly faster than the GamePad in the search for objects. Experienced users performed significantly better when using the GamePad, compared to casual gamers. When casual gamers were analyzed separately, their performance was better (at a statistically significant level) with the multi-touch display, compared to the GamePad. Finally, users had a significantly higher error rate when switching from the GamePad to the keyboard, than when switching from the multi-touch to the keyboard.

We present a whole-body gesture elicitation study using Head Mounted Displays, including a legacy bias reduction technique. The motivation for this study was to understand the type of gesture agreement rates for selection and manipulation interactions and to improve the user experience for whole-body interactions. We found that regardless of the production technique used to remove legacy bias, legacy bias was still found in some of the produced gestures.

We present a whole-body gesture elicitation study using Head Mounted Displays, including a legacy bias reduction. The motivation for this study was to understand the type of gesture agreement rates for selection and manipulation interactions and to improve the user experience for whole-body interactions. We looked at 23 participants and 20 distinct referents (with multiple gestures per referent). We found that regardless of the production technique used to remove legacy bias, legacy bias was still found in some of the produced gestures. In some instances, gestures were derived from previous interactions but were still appropriate for the environment presented. This study provides a rich set of information and useful recommendations for future designers and/or developers.

Understanding gaming expertise is important in user studies. We present a study comprised of 60 participants playing a First Person Shooter Game (Counter-Strike: Global Offensive). This study provides results related to a keyboard model used to determine an objective measurement of gamers' skill. We also show that there is no correlation between frequency questionnaires and user skill.

CircGR is a multi-touch non-symbolic gesture recognition algorithm, which utilizes circular statistic measures to implement linearithmic (O(n log n)) template-based matching. CircGR provides a solution to gesture designers, which allows for building complex multi-touch gestures with high-confidence accuracy. We demonstrated the algorithm and described a user study with 60 subjects and over 12,000 gestures collected for an original gesture set of 36. The accuracy is over 99% with the Matthews correlation coefficient of 0.95. In addition, early gesture detection was successful in CircGR as well.