Continuous motion detection and recognition method based on CNN and inertial sensor
To improve accuracy and efficiency of the continuous human motion detection and recognition,a recognition and detection method based on convolutional neural network(CNN)is proposed.Firstly,motion acceleration data are collected through a 3-axis acceleration sensor,and acceleration spectrum and the characteristic matrix of motion pattern recognition are obtained through filtering,sliding time window and fast Fourier transform(FFT).Through testing,analysis and research on network models with multiple structures and parameters,a CNN structure with a double-layer 33 convolution kernel convolutional layer and a double-layer 256 × 64 neuron dense layer is selected to recognize 5 typical exercises of stand-up,walk-up,upstairs,downstairs and running and 2 abnormal exercises of falling and lying down,respectively.The results show that the recognition accuracy of 7 kinds of movements,such as falling and lying,is greater than 95％,and it is an effective method for the detection and recognition of human continuous motion.At the same time,the method has a low demand for computing power and can be deployed in low power consumption on mobile platforms.
万方数据
维普
