Implementation of a batch normalized deep LSTM recurrent network on a smartphone for human activity recognition

In this paper we present a Long-Short Term Memory (LSTM) deep recurrent neural network (RNN) model for the classification of human daily life activities by using the accelerometer and gyroscope data of a smartphone. The proposed model was trained by using the open-source TensorFlow library, optimised and deployed on an Android smartphone. Hardware resource requirements for the implementation are empirically investigated and the effect of data quantization on the accuracy of the implementation is discussed. In addition, we profile the power budget for running the proposed model on smartphone. Results of this work will be of use for deep learning implemented on edge computing devices, which leverages the user privacy as the raw data never leaves the person