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• W2895943783 abstract "Due to recent improvements in Virtual Reality (VR) regarding the potential of full-body tracking, the number of VR-based exergames has been increasing. However, such applications often depend on additional tracking technology, e.g., markerless or marker-based. On the one hand, tracking approaches, such as the Kinect device are limited by either high latency or insufficient accuracy. On the other hand, motion capture suits are expensive and create discomfort. In this paper we present an accurate motion recognition approach, using only the HTC Vive HMD with their associated Controllers and Trackers. The recognition is based on an Hidden Markov Model, that has been trained in advance for a specific movement. The results suggest that our system is capable of detecting a complex full-body gesture, such as yoga Warrior I pose, with an accuracy of 88%. In addition, audible feedback is provided, so that the user can immediately hear if the particular exercise has been executed correctly. Such a system can be used to assist players in learning a particular movement and can be applied in various serious games applications, e.g., for training purposes or rehabilitation." @default.
• W2895943783 created "2018-10-26" @default.
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• W2895943783 date "2018-01-01" @default.
• W2895943783 modified "2023-10-06" @default.
• W2895943783 title "Recognition of Full-Body Movements in VR-Based Exergames Using Hidden Markov Models" @default.
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• W2895943783 doi "https://doi.org/10.1007/978-3-030-02762-9_20" @default.
• W2895943783 hasPublicationYear "2018" @default.
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