Implementation Project for Detect-Heavy-Drinking-Episodes paper

Implementation of Research work in "Learning to Detect Heavy Drinking Episodes Using Smartphone Accelerometer Data" - Jackson A Killian et al.

Requirements

1. Pandas
2. Scikit
3. Scipy
4. Numpy
5. Other Python3 libs

Dataset:

http://archive.ics.uci.edu/ml/datasets/Bar+Crawl%3A+Detecting+Heavy+Drinking

References:

http://ceur-ws.org/Vol-2429/paper6.pdf
https://github.com/tyiannak/pyAudioAnalysis
https://web.cs.wpi.edu/~emmanuel/publications/PDFs/C17.pdf
https://librosa.github.io/librosa/index.html

How to run:

All the features generated are saved as pickle files. Hence we can directly run the classifier and see the output.
So if you just want to run the classifier:
$ python3 rft.py

If you want to generate all the features again (This may take close to a few hour depending on the CPU):
$ python3 eda_edits_copy.py
$ python3 rft.py