Soft Smart Biopatch for Continuous Authentication‐Enabled Cardiac Biometric Systems

Author:

Lee Sung Hoon12ORCID,Lee Yoon Jae12,Kwon Kangkyu12,Lewis Daniel23,Romero Lissette24,Lee Jimin25,Zavanelli Nathan25,Yan Emily23,Yu Ki Jun6,Yeo Woon‐Hong2357ORCID

Affiliation:

1. School of Electrical and Computer Engineering Georgia Institute of Technology Atlanta GA 30332 USA

2. IEN Center for Human‐Centric Interfaces and Engineering Institute for Electronics and Nanotechnology Georgia Institute of Technology Atlanta GA 30332 USA

3. Wallace H. Coulter Department of Biomedical Engineering Georgia Tech and Emory University School of Medicine Atlanta GA 30332 USA

4. School of Industrial Design Georgia Institute of Technology Atlanta GA 30332 USA

5. George W. Woodruff School of Mechanical Engineering Georgia Institute of Technology Atlanta GA 30332 USA

6. School of Electrical and Electronic Engineering Yonsei University Seoul 03722 Republic of Korea

7. Parker H. Petit Institute for Bioengineering and Biosciences Institute for Materials Neural Engineering Center Institute for Robotics and Intelligent Machines Georgia Institute of Technology Atlanta GA 30332 USA

Abstract

AbstractBiometric locking systems offer a seamless integration of an individual's physiological characteristics with secure authentication. However, they suffer from limitations such as false positive and negative authentication, environmental interference, and varying disadvantages across multiple authentication methods. To address these limitations, this study develops a soft smart biopatch for a continuous cardiac biometric wearable device that can continuously gather novel biometric data from an individual's heart sound for authentication with minimal error (less than 0.5%). The device is designed to be discreet and user‐friendly, and it employs soft biocompatible materials to ensure comfort and ease of use. The patch system incorporates a miniaturized microphone to monitor sounds over long periods and multiple dimensions, enhancing the reliability of the biometric data. Furthermore, the use of machine‐learning algorithms has enabled the creation of unique identification keys for individuals based on the continuous monitoring properties of the low‐cost device. These advantages make it more effective and efficient than traditional biometric systems, with the potential to enhance the security of mobile devices and door locks.

Funder

National Science Foundation

Publisher

Wiley

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