Human Activity Recognition via MobileNet-SVM

The repo is about performing classification on sensory data generated via smartphone on different types of human activities for example: sitting, standing, lying, walking, jogging, climbing upstairs and downstairs.

Dataset

The dataset of this project is available here: UCI-HAR Dataset: https://drive.google.com/open?id=1of3MoPCP04TlTCZPhuKTEl1oA0wqXbuu WISDM Dataset: https://drive.google.com/open?id=1JmzFS86QdwBYMyubFfWU1pf8mMgiw_19

How to execute the file

1. Git clone or download this repo.
2. Download and extract all files from the links provided in Dataset into the folder name "HARDataset" and "WISDMData" respectively.
3. To Perform classification on UCI-HAR dataset: python main_UCIHAR
4. Perform classification on WISDM dataset: python main_WISDM

References

1. 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.

   https://archive.ics.uci.edu/ml/datasets/human+activity+recognition+using+smartphones

2. Jennifer R. Kwapisz, Gary M. Weiss and Samuel A. Moore (2010). Activity Recognition using Cell Phone Accelerometers, Proceedings of the Fourth International Workshop on Knowledge Discovery from Sensor Data (at KDD-10), Washington DC

   http://www.cis.fordham.edu/wisdm/dataset.php