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Wearable Ubiquitous Technol."],"published-print":{"date-parts":[[2018,3,26]]},"abstract":"<jats:p>The paper explores the possibility of using wrist-worn devices (specifically, a smartwatch) to accurately track the hand movement and gestures for a new class of immersive, interactive gesture-driven applications. These interactive applications need two special features: (a) the ability to identify gestures from a continuous stream of sensor data early--i.e., even before the gesture is complete, and (b) the ability to precisely track the hand's trajectory, even though the underlying inertial sensor data is noisy. We develop a new approach that tackles these requirements by first building a HMM-based gesture recognition framework that does not need an explicit segmentation step, and then using a per-gesture trajectory tracking solution that tracks the hand movement only during these predefined gestures. Using an elaborate setup that allows us to realistically study the table-tennis related hand movements of users, we show that our approach works: (a) it can achieve 95% stroke recognition accuracy. 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