Recognition of Human Activities by Smartphone Sensors Using LSTM Neural Network
Hong Ping Zhao, Chunning Hou, Donglin Ma · 2018
Human activities have been a hot research field.Many sensors are embedded in the smartphone, which makes mobile sensor become available.Sensors of smartphone can early get the human activities information, which can analyze the human behaviors and provide the useful information to the human.In this paper, we propose a new method to recognize the human activities, which is based on the LSTM neural network to extract features and classify using accelerometer sensor data and gyroscope sensor data.Experimental results show that using LSTM neural network and TensorFlow deep learning open source architecture to extract motion state characteristics, this method achieves human activities classification with an accuracy of up to 90.4%.