Journal of Chaohu University ›› 2018, Vol. 20 ›› Issue (6): 84-90.
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This device takes STM32 MCU as the control core and realizes gesture training and recognition based on FDC2214 as the main module. The four plates are used to divide the proportional region for judgment, which is helpful for gesture judgment,reducing randomness error and enhancing reliability. In the device, Kalman filter algorithm is used for waveform filtering processing to alleviate the roughness of waveform measurement and bubble sorting algorithm is adopted to judge the difference value and strengthen the measurement accuracy. A gesture testing platform is built and corresponding data is tested. The results show that the designed gesture recognition device can train and distinguish gestures and complete the requirements of the question.
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