Abstract
Electrocardiogram (ECG) signals sensed from mobile devices pertain the potential for biometric identity recognition applicable in remote access control systems where enhanced data security is demanding. In this study, we propose a new algorithm that consists of a two-stage classifier combining random forest and wavelet distance measure through a probabilistic threshold schema, to improve the effectiveness and robustness of a biometric recognition system using ECG data acquired from a biosensor integrated into mobile devices. The proposed algorithm is evaluated using a mixed dataset from 184 subjects under different health conditions. The proposed two-stage classifier achieves a total of 99.52% subject verification accuracy, better than the 98.33% accuracy from random forest alone and 96.31% accuracy from wavelet distance measure algorithm alone. These results demonstrate the superiority of the proposed algorithm for biometric identification, hence supporting its practicality in areas such as cloud data security, cyber-security or remote healthcare systems.
Highlights
Mobile devices are indispensable in our daily lives in social networking, ecommerce, online banking, and remote healthcare
Points,asaswell well features extracted we first assume thatassume a singlethat random forest algorithm is used and would be and first would independently purposes, we first a single random forest algorithm is used be first evaluated in order to explore howto the subjecthow verification accuracy changes when changes the ECGwhen features independently evaluated in order explore the subject verification accuracy the are different from combinations fiducial points
The main goal of this study is to facilitate the application of mobile ECG for biometric subject verification for applications where data security is demanding
Summary
Mobile devices are indispensable in our daily lives in social networking, ecommerce, online banking, and remote healthcare. A large amount of personal data is stored in the cloud and can be accessed anywhere around the globe. This poses a major concern in data security and confidentiality [1,2]. Low-power and small-size biosensors are being integrated into mobile devices for real-time monitoring of people’s physiological conditions during daily activities, including electrocardiograms. This makes mobile ECG data readily available for biometric recognition without added hardware cost
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