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Advances in Multimedia
Volume 2007, Article ID 82638, 11 pages
Research Article

MOCA: A Low-Power, Low-Cost Motion Capture System Based on Integrated Accelerometers

1Dipartimento di Elettronica, Informatica e Sistemistica (DEIS), Universitá di Bologna, Viale Risorgimento 2, Bologna 40136, Italy
2Information Science and Technology Institute (ISTI), University of Urbino, Piazza Repubblica 13, Urbino 61029, Italy

Received 5 September 2006; Revised 18 January 2007; Accepted 8 March 2007

Academic Editor: Yong Pei

Copyright © 2007 Elisabetta Farella et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Human-computer interaction (HCI) and virtual reality applications pose the challenge of enabling real-time interfaces for natural interaction. Gesture recognition based on body-mounted accelerometers has been proposed as a viable solution to translate patterns of movements that are associated with user commands, thus substituting point-and-click methods or other cumbersome input devices. On the other hand, cost and power constraints make the implementation of a natural and efficient interface suitable for consumer applications a critical task. Even though several gesture recognition solutions exist, their use in HCI context has been poorly characterized. For this reason, in this paper, we consider a low-cost/low-power wearable motion tracking system based on integrated accelerometers called motion capture with accelerometers (MOCA) that we evaluated for navigation in virtual spaces. Recognition is based on a geometric algorithm that enables efficient and robust detection of rotational movements. Our objective is to demonstrate that such a low-cost and a low-power implementation is suitable for HCI applications. To this purpose, we characterized the system from both a quantitative point of view and a qualitative point of view. First, we performed static and dynamic assessment of movement recognition accuracy. Second, we evaluated the effectiveness of user experience using a 3D game application as a test bed.