ECG in Real World Scenario: Time Variability in Biometric using Wearable Smart Textile Shirts

Abstract

Biomedical signals, such as an electrocardiogram (ECG), have been included in wearable platforms for biometric reasons due to the rapid expansion of apps and technology capable of gathering this physiological data. Most studies using ECG biometrics are conducted in clinical settings, which is impractical for wearable ECG-based biometric applications. Therefore, this study aims to determine the reliability of ECG signals obtained from the commercially available Hexoskin Proshirt and HeartIn Fit shirt, which may be worn for biometric verification in real-world scenarios. ECG data from 22 participants were collected over a span period of more than 30 days. The raw ECG signal is first pre-processed in the time domain using noise-removal Butterworth filters, and then a successful QRS segmented feature extraction method is used. Not to mention, 300 datasets were used to test the recommended recognition method using a Quadratic Support Vector Machine (QSVM). In comparison, around 854 datasets were prepared for training and validation of the classifier. The findings showed that the proposed method provided a considerable accuracy above 99.63 % with a FAR of 0.14 %, an FRR of 2.86 %, and a TPR of 97.14 %. Thus, the study supports using ECG biometrics for verification in real-world settings by employing a smart textile shirt with varying temporal variability.