Predicting Driver Stress Using Deep Learning
About the project
This project is part of my masters thesis and aims to predict driver stress levels using Physionet's SRAD (drivedb) dataset with methods such as LSTMs, RNNs and CNNs. It is a work based predominantly on Healey's paper titled "Detecting Stress During Real-World Driving Tasks Using Physiological Sensors" which can be found here.\ The dataset contains inputs collected from physiological signals such as: Electrocardiogram, Electromyogram, and Galvanic Skin Response.
Getting started
The scripts are sorted in an alphabetical manner to make it easier for anyone to run the codes. Hence, if one is interested in running the c_preprocessing.py script, the data has to be provided as in the proceding scripts, i.e: a_convert_to_csv.py.
Convert the dataset
To convert the files from .dat format to csv, you can run the a_convert_to_csv.py file. This can be done by calling the convert_to_csv function with default parameters.
Get basic plots and info
To plot the data for each drive, the b_basic_information.py script can be utilized.
Preprocessing
The initial dataset does not contain the marker data. To attach the marker data to each csv, the process_data function from c_preprocessing.py script can be used.
Feature Selection
The dataset consists of 7 columns, namely: ECG, EMG, hGSR, fGSR, HR, RESP. To create a set of 22 features, d_feature_selection.py can be run which will create the following columns: EMG_mean, footGSR_mean, footGSR_std, footGSR_frequency, footGSR_magnitude, footGSR_duration, footGSR_area, handGSR_mean,handGSR_std, handGSR_frequency, handGSR_magnitude, handGSR_duration, handGSR_area, HR_mean, HR_std, HRV_ratio, RESP_mean, RESP_std, RESP_ulf, RESP_vlf, RESP_lf, RESP_hf, Stress.\
Moreover, the data can be segmented into different intervals. The default value is 10 seconds.
Algorithms
Lastly, this project aims to predict stress levels using various reduction techniques with the aid of deep learning models such as LSTMs, CNN and RNNs. These techniques can be accessed in the e_models.py file.
Disclaimer
Please bear in mind that some of the default values for the functions may not directly mean best values or parameters because I have been playing with them. However, for a more detailed description of the parameters, you are encouraged to read my thesis by clicking here.
Feature Selection Infographics
Galvanic Skin Response Features
Heart Rate Features
Resp Features
License
Distributed under the MIT License. See LICENSE for more information.