BIOWISH: Biometric Recognition Using Wearable Inertial Sensors Detecting Heart Activity

• Published in: IEEE transactions on dependable and secure computing Vol. 21; no. 2; pp. 987 - 1000
• Main Authors: Maiorana, Emanuele, Romano, Chiara, Schena, Emiliano, Massaroni, Carlo
• Format: Journal Article
• Language: English
• Published: Washington IEEE 01.03.2024; IEEE Computer Society
• Subjects: Activity recognition; Biomedical monitoring; Biometrics; Cybersecurity; deep learning; Electrical measurement; Electrocardiography; explainability; Feature extraction; gyrocardiography; Heart; Inertial sensing devices; Machine learning; Mechanical measurement; Performance evaluation; Seismocardiography; Sensors; Wearable devices; Wearable technology
• ISSN: 1545-5971; 1941-0018; 2160-9209; 1941-0018
• DOI: 10.1109/TDSC.2023.3268360

Wearable devices have been recently proposed to perform biometric recognition, leveraging on the uniqueness of the collectable physiological traits to generate discriminative identifiers. Most of the studies conducted on this topic have exploited heart-related signals, sensing the cardiac activity either through electrical measurements using electrocardiography, or with optical recordings employing photoplethysmography. In this article we instead propose a system performing BIOmetric recognition using Wearable Inertial Sensors detecting Heart activity (BIOWISH). In more detail, we investigate the feasibility of exploiting mechanical measurements obtained through seismocardiography and gyrocardiography to verify the identity of a subject. Several feature extractors and classifiers, including deep learning techniques relying on siamese training, are employed to derive distinctive characteristics from the considered signals, so as to differentiate between legitimate users and impostors. A multi-session database, comprising acquisitions taken from subjects performing different activities, is employed to perform experimental tests. The obtained results testify that identifiers derived from measurements of chest vibrations, collected by wearable inertial sensors, could be employed to guarantee high recognition performance, even when considering short-time recordings. Explainability methods have been also employed to derive some insights about the aspects relevant to perform predictions for both people and activity recognition tasks.