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.