By using LSTM RNN networks we were able to classify human movement amongst six categories:
WALKING, WALKING_UPSTAIRS, WALKING_DOWNSTAIRS, SITTING, STANDING, LAYING.
The dataset was collected from 30 persons(referred as subjects in this dataset), performing different activities with a smartphone to their waists. The data is recorded with the help of sensors (accelerometer and Gyroscope) in that smartphone. This experiment was video recorded to label the data manually.
We were able to achieve an accuracy of 95%. The loss value measured is 0.1142
The dataset can be found on the UCI Machine Learning Repository : https://archive.ics.uci.edu/ml/datasets/Human+Activity+Recognition+Using+Smartphones
Davide Anguita, Alessandro Ghio, Luca Oneto, Xavier Parra and Jorge L. Reyes-Ortiz. A Public Domain Dataset for Human Activity Recognition Using Smartphones. 21th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2013. Bruges, Belgium 24-26 April 2013
The code was partially inspired by https://github.com/guillaume-chevalier/LSTM-Human-Activity-Recognition