Gesture-Based, Touch-Free Multi-User Gaming on Wall-Sized, High-Resolution Tiled Displays

Having to carry input devices can be inconvenient when interacting with wall-sized, high-resolution tiled displays. Such displays are typically driven by a cluster of computers. Running existing games on a cluster is non-trivial, and the performance attained using software solutions like Chromium is not good enough. This paper presents a touch-free, multi-user, humancomputer interface for wall-sized displays that enables completely device-free interaction. The interface is built using 16 cameras and a cluster of computers, and is integrated with the games Quake 3 Arena (Q3A) and Homeworld. The two games were parallelized using two different approaches in order to run on a 7x4 tile, 21 megapixel display wall with good performance. The touch-free interface enables interaction with a latency of 116 ms, where 81 ms are due to the camera hardware. The rendering performance of the games is compared to their sequential counterparts running on the display wall using Chromium. Parallel Q3A’s framerate is an order of magnitude higher compared to using Chromium. The parallel version of Homeworld performed on par with the sequential, which did not run at all using Chromium. Informal use of the touch-free interface indicates that it works better for controlling Q3A than Homeworld.

Estimating Gesture Accuracy in Motion-Based Health Games

This manuscript details a technique for estimating gesture accuracy within the context of motion-based health video games using the MICROSOFT KINECT. We created a physical therapy game that requires players to imitate clinically significant reference gestures. Player performance is represented by the degree of similarity between the performed and reference gestures and is quantified by collecting the Euler angles of the player's gestures, converting them to a three-dimensional vector, and comparing the magnitude between the vectors. Lower difference values represent greater gestural correspondence and therefore greater player performance. A group of thirty-one subjects was tested. Subjects achieved gestural correspondence sufficient to complete the game's objectives while also improving their ability to perform reference gestures accurately.

FiFi – Robuste Personenerkennung gestengesteuerter Fahrzeuge in der Intralogistik

Die Steuerung des Fahrerlosen Transportfahrzeuges „FiFi“ erfolgt berührungslos durch Gesten- und Personenerkennung basierend auf 3D-Daten der Umgebung. Die genutzten Verfahren zur Personenerkennung führen in einigen Fällen zur Falsch-Erkennung von Personen in Objekten. Das Paper beschreibt die Ursachen der Fehlerkennung und stellt die umgesetzten Lösungsansätze zur Vermeidung vor. Experimente bestätigen, dass die entwickelten Verfahren die Robustheit des Systems erhöhen.