Gesture recognition with a Wii controller

By T. Schlömer, Benjamin Poppinga, Niels Henze, Susanne CJ Boll. International Conference on Tangible, Embedded, and Embodied Interaction, 2008

551 citations recorded by the source corpus.

In many applications today user interaction is moving away from mouse and pens and is becoming pervasive and much more physical and tangible. New emerging interaction technologies allow developing and experimenting with new interaction methods on the long way to providing intuitive human computer interaction. In this paper, we aim at recognizing gestures to interact with an application and present the design and evaluation of our sensor-based gesture recognition. As input device we employ the Wii-controller (Wiimote) which recently gained much attention world wide. We use the Wiimote's acceleration sensor independent of the gaming console for gesture recognition. The system allows the training of arbitrary gestures by users which can then be recalled for interacting with systems like photo browsing on a home TV. The developed library exploits Wii-sensor data and employs a hidden Markov model for training and recognizing user-chosen gestures. Our evaluation shows that we can already recognize gestures with a small number of training samples. In addition to the gesture recognition we also present our experiences with the Wii-controller and the implementation of the gesture recognition. The system forms the basis for our ongoing work on multimodal intuitive media browsing and are available to other researchers in the field.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Human-Computer Interaction

View Paper: https://www.semanticscholar.org/paper/90465c8c7a79474a509d31f67d5d62d7257a2517

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Citations in the corpus

MEMS Accelerometer Based Nonspecific-User Hand Gesture Recognition (2012, background): “It is effective for recognizingpatterns with spatial and temporal variation [17].”
First-Person View Hand Segmentation of Multi-Modal Hand Activity Video Dataset (2020, background): “Therefore, understanding hands with computer vision systems has been deeply explored through hand tracking [41, 54], hand pose estimation [18, 21, 22, 27, 29], grasp detection [10, 47], hand gesture recognition [58], multi-view prediction [29], and hand-action classiﬁcation [50].”
Vision-based hand-gesture applications (2011, background): “Schlömer et al.(42) addressed accelerometer-based gesture recognition for drawing and browsing operations in a computer game.”