Gesture interaction is a commonly used solution when introducing Natural User Interface (NUI), a kind of user interface where the interaction is direct and consistent with natural heuristic behaviors. In this paper, a smart glove with efficient real-time hand orientation calculation and accurate static hand gesture prediction is proposed. This custom-built wireless glove consists of flex sensors, an Inertial Measurement Unit (IMU) sensor, a microcontroller with multi-channel ADC/AMP (analog to digital converter and amplifier), a Bluetooth module, and an Arduino Micro Pro. K-Nearest-Neighbor (KNN) classifier is implemented to assist static hand gesture prediction with the validated accuracy exceeding 97%. This supervised machine learning algorithm allows a highly customizable smart glove which the input gestures, input number of gestures, and the associated activating functions are all easily changeable by the users any time. To show the benefits of combining the NUI and supervised machine learning, a validation experiments, computer control, were conducted.

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