An android application to predict human activity using a deep learning LSTM model

Abstract

The machine learning approach to estimate human activity using smartphone sensor data is challenging. In this project, the HAR approach is conducted based on the LSTM model and can recognize six different behaviors, i.e., Downstairs, Jogging, Sitting, Standing, Upstairs, and Walking. To achieve the best potential result, various machine learning and statistical approaches were explored. The long shortterm memory (LSTM) is a recurrent neural networks (RNNs) capable of learning long-term dependencies, especially in sequence prediction problems. This LSTM model was applied in this project, to obtain the desired result. This model shows 97% test accuracy. Finally, the model was exported and deployed in the Android application, which has an user interface that could provide a user-friendly experience.