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Tangible User Interfaces (TUIs) feature physical objects that people can manipulate to interact with smart spaces. Smart objects used as TUIs can further improve user experience by recognizing and coupling natural gestures to commands issued to the computing system. Hidden Markov Models (HMM) are a typical approach to recognize gestures sampled from inertial sensors. In this paper, the authors implement a HMM-based continuous gesture recognition algorithm, optimized for lowpower, low-cost microcontrollers without floating point unit. The proposed solution is validated on a set of gestures performed with the Smart Micrel Cube (SMCube), which embeds a 3-axis accelerometer and an 8-bit microcontroller.
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