Activity Recognition using 1D convolution from Accelerometers Data

Abstract

Aiming at the problem of activity a recognition method based on a convolutional neural network was proposed in this papaer, which can effectively classify 6 types of human movements: Downstaris, Jogging, Sitting, Standing, Upstairs and Working. The network consists of an input layer, two convolutional layers, two pooling layers, a fully connected layer, and an output layer. The sliding window is used to transform the sensor data into a three-channel RGB image format, and the features of the three-axis speed sensor data are automatically extracted to classify each action. The model was reproduced using Tensorflow, and the recognition rate of 89.35% was achieved on the open source database WISDM. The experimental results show that the amplifier has a better effect on human movement recognition.