Person identification using heart rate and activity from consumer-grade wearables

Person identification using heart rate and activity from consumer-grade wearables: How do different types of cardiac diagnosis affect the accuracy of Deep Neural Networks to identify individuals by their heart rate?

Harthoorn, Jip (TU Delft Electrical Engineering, Mathematics and Computer Science)

Tax, D.M.J. (mentor)
Naseri Jahfari, A. (mentor)
Ghorbani, R. (mentor)
Lan, G. (graduation committee)

Delft University of Technology

Computer Science and Engineering

CSE3000 Research Project

2023-06-30

Advancements in the precision and accuracy of consumer-grade wearables, such as a Fitbit, have enabled the identification and therefore authentication of individuals based on their emitted heart frequencies using these wrist-worn devices. With this type of authentication, a password is essentially sent out every second. This makes it a perfect form of authentication in fields where constant authentication is crucial. However, not much is known about how different types of cardiac diagnosis (e.g. fit or obese) influence the accuracy of this type of authentication. In this paper, it will be shown how different types of cardiac diagnosis affect the accuracy of Deep Neural Networks to identify individuals by heart rate. This study is done with data obtained from 14 subjects, having different types of cardiac diagnosis. A deep neural network consisting of multiple convolutional layers is being used to conduct the experiments. It has been shown that subjects with a paroxysmal atrial fibrillation diagnosis improve the accuracy the most, compared to the reference (normal healthy) subjects. On the other hand, (very) fit subjects decrease the accuracy the most. Heart failure and obese subjects have a similar accuracy compared to reference subjects.

Person identification
Heart rate
Cardiac diagnosis

http://resolver.tudelft.nl/uuid:5cb751ae-58ba-43d0-870d-0933827a5662

Student theses

bachelor thesis

© 2023 Jip Harthoorn