TECHNIQUES OF PERFORMING CONVOLUTIONAL NEURAL NETWORK-BASED GESTURE RECOGNITION USING INERTIAL MEASUREMENT UNIT

摘要

A gesture recognition system is provided. The disclosed gesture recognition system includes a sensor module including at least one Inertial Measurement Unit (IMU) for sensing data about a gesture, and data sensed based on a convolutional neural network (CNN). A CNN-based gesture recognition module for recognizing a gesture from gesture data, wherein each of the at least one IMU comprises an accelerometer, a gyroscope and a geomagnetic sensor, wherein the CNN includes at least one convolutional layer, at least one pooling layer, At least one feedforward neural network layer and another feedforward neural network layer for the final output of the CNN, wherein the activation function of the at least one convolutional layer is a step function, and the activation function of the at least one feedforward neural network layer is Rectified Linear Unit (ReLU) function.