IIUM Repository

Biometric analysis on smart textile garment in real life scenario

Mohd Nawawi, Muhammad Muizz and Sidek, Khairul Azami and Azman, Amelia Wong (2024) Biometric analysis on smart textile garment in real life scenario. Journal of Advanced Research in Applied Sciences and Engineering Technology, 34 (1). pp. 372-386. ISSN 2462-1943

PDF - Published Version
Download (798kB) | Preview
Download (88kB) | Preview


The rapid proliferation of wearable applications and technologies capable of acquiring biomedical signals has prompted the incorporation of biomedical signals, such as the electrocardiogram (ECG), for biometric purposes in wearable platforms. Most ECG biometric research utilises medical-grade sensors in clinical settings, which is unrealistic for wearable ECG-based biometric applications in the real world. Therefore, this research aims to examine the ECG biometric on smart textile garments in real life, collected from commercially available wearable Hexoskin Proshirt and HeartIn Fit shirts. ECG data were obtained from 22 participants who took part in this study. The raw ECG signal is initially pre-processed using noise-removal Butterworth filters in the timedomain, followed by an effective QRS segmented feature extraction technique. Finally, around 2076 datasets were created for training and validation, while the remaining 501 datasets were employed to test the suggested recognition approach with 29 Machine Learning Classifiers. Subsequently, Quadratic SVM has the highest accuracy at 96.8% for ECG biometrics, followed by Narrow Neural Network with 95.8% and Wide Neural Network with 95.4%. Further improvement to the QSVM parameter improved the accuracy to 97.4% with an error rate of 2.6%, followed by a sensitivity of 97.4% with a precision of 97.7% and a false rejection rate of 2.6%. Thus, the results of this study further validate the feasibility of applying ECG biometrics for recognition in real-life scenarios utilising a smart textile shirt with different configurations and brand is possible.

Item Type: Article (Journal)
Additional Information: 4698/111163
Uncontrolled Keywords: ECG; Biometric; Smart Textile; Wearable; Smart Garment; Machine Learning
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7800 Electronics. Computer engineering. Computer hardware. Photoelectronic devices > TK7885 Computer engineering
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button): Kulliyyah of Engineering > Department of Electrical and Computer Engineering
Depositing User: Assoc Prof Dr Khairul Azami Sidek
Date Deposited: 06 Mar 2024 12:45
Last Modified: 06 Mar 2024 16:26
URI: http://irep.iium.edu.my/id/eprint/111163

Actions (login required)

View Item View Item


Downloads per month over past year