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Hand Gesture Classification using Inaudible Sound with Ensemble Method
2020
Advances in Science, Technology and Engineering Systems
Recognizing the human behavior and gesture has become important due to the increasing use of wearable devices. This study classifies hand gestures by creating sound in the inaudible frequency range from a smartphone and analyzing the reflected signals. We convert the sound using Short-Time Fourier Transform to magnitude and phase. We trained two types of data on Convolutional Neural Network model. And then we propose a method applying soft voting, an ensemble technique, to improve
doi:10.25046/aj0506115
fatcat:ddtn672zbnd7zfwt4mpjeduyiu