Computer mice are widely used today as the primary input device in competitive video games. If a player exhibits more wrist rotation than other players when moving the mouse laterally, the player is said to have stronger wrist-aiming habits. Despite strong public interest, there has been no affordable technique to quantify the extent of a player’s wrist-aiming habits and no scientific investigation into how the habits affect player performance and workload. We present a reliable and affordable technique to quantify the extent of a player’s wrist-aiming habits using a mouse equipped with two optical sensors (i.e., a dual-sensor mouse). In two user studies, we demonstrate the reliability of the technique and examine the relationship between wrist-aiming habits and player performance or workload. In summary, player expertise and mouse sensitivity significantly impacted wrist-aiming habits; the extent of wrist-aiming showed a positive correlation with upper limb workload.

In this study, we analyzed how the performance of professional Go players has changed since the advent of AlphaGo, the first artificial intelligence (AI) application to defeat a human world Go champion. We interviewed and surveyed professional Go players and found that AI has been actively introduced into the Go training process since the advent of AlphaGo. The significant impact of AI-based training was confirmed in a subsequent analysis of 6,292 games in Korean Go tournaments and Elo rating data of 1,362 Go players worldwide. Overall, the tendency of players to make moves similar to those recommended by AI has sharply increased since 2017. The degree to which players’ expected win rates fluctuate during a game has also decreased significantly since 2017. We also found that AI-based training has provided more benefits to senior players and allowed them to achieve Elo ratings higher than those of junior players.

Most virtual reality (VR) experiences are held in limited physical space; therefore, increasing the physical space's spatial efficiency is an essential task for the VR industry. Redirected walking maps a virtual path and a real path with unnoticeable distortion, enabling users to walk through a much bigger virtual space than physical space. To hide the distortion from the user, detection thresholds have been measured, entirely focusing on forward steps. However, it is not uncommon for the user to walk non-forward, that is, sideward and backward in VR. In addition to a forward step, adding options for a non-forward step can expand the VR locomotion in any direction. In this work, we measure the translation and curvature detection thresholds for non-forward steps. The results show similar translation detection thresholds with forward-step and wider detection thresholds for the curvature gain in both backward and sideward step experiments. Having sideward and backward steps in the redirected walking arsenal can add freedom to virtual world design and lead to efficient space usage.