We implemented user authentication using ECG acquired from a heart rate sensor. The authentication pipeline mainly contains four parts: data acquisition, data preprocessing, feature extraction and authentication(classification).
First, please prepare the ECG data by referring to the Data Acquisition chapter below. Second, please run the python script main.py.
$ python3 main.py
As a result, the terminal will display the authentication results as shown below.For the meaning of the authentication result, please read the Authentication chapter. In addition, each process of authentication saves the calculation results in its own directory.
$ python3 main.py template : user1 test user : score user1 : 98.74 [%] user0 : 97.57 [%] user2 : 96.21 [%] -------------- template : user0 test user : score user0 : 91.59 [%] user2 : 90.05 [%] user1 : 89.24 [%] -------------- template : user2 test user : score user2 : 98.41 [%] user1 : 96.98 [%] user0 : 96.86 [%] --------------
Data acquisition module aims to obtain raw biometric signals, either obtained from wearable devices or directly acquired from available public datasets 1.
To obtain raw ECG signal, we used a DFRobot's Gravity series of heart rate sensor and Arudino Uno. You can get the code here to read serial data and save as csv file with Python. The data and file format should follow the contents of format.txt. The directory structure should be as follows.
data
├── features
├── raw
├── result
└── templates
Data preprocessing module is applied to filter high-frequency noise and segment signals into periodic cycles.The first step in data preprocessing is signal filtering, which filters the high frequency noise caused by unstable measurement circum stances in the signal capturing stage. Then, filtered signals are segmented into cycles with the same time interval. 1
We constructed our preprocessing, segmentation, and validation pipelines around the Python library neurokit. The code for these operations can be found in signal_process.py. The preprocess and get_segment functions in signalprocess.py have an argument graph_show which, if set to True, will display the following graphs. These graphs are provided by the neurokit library.
Feature extraction module aims to extract useful features for classification1.
We implemented three features
- Fiducial Point (QRS waveform)
- Mel-Frequency Cepstral Coefficients (MFCC)
- Autocorrelation Coefficients (ACC).
as in reference 2. The functions to compute these features are implemented in signal_process.py.
Classification module utilizes template matching method, machine learning algorithm, or deep neural network to classify the given biometrics signals and returns the authentication result1.
We used the template matching method for the authentication process. The average value of the Fiducial Point feature (QRS waveform) at each time was used as the signal template. The similarity between the signal template and the signal representation was calculated through DTW distance. These authentication processes are implemented in create_template.py and authentication.py. However, you should be aware of a few things about the get_dtw function in autenticaiton.py. First, the DTW distance is normalized by the longer of the two signals being compared. Furthermore, the similarity is implemented as follows
When you run main.py, you will see the DTW based similarity sorted in descending order. The top one is the user who has the highest similarity to the template. In the case shown below, all authentication is successful. For example, when user1 is the template, the similarity with the test data of user1 is 98.74%, which is higher than the test data of other users.
$ python3 main.py template : user1 test user : score user1 : 98.74 [%] user0 : 97.57 [%] user2 : 96.21 [%] -------------- template : user0 test user : score user0 : 91.59 [%] user2 : 90.05 [%] user1 : 89.24 [%] -------------- template : user2 test user : score user2 : 98.41 [%] user1 : 96.98 [%] user0 : 96.86 [%] --------------