Feasibility of Wrist-Worn, Cancelable, Real-Time Biometric Authentication via HD-sEMG and Dynamic Gestures

In recent years, high-density surface electromyography (HD-sEMG) has been applied for cancelable authentication of wearable devices used in healthcare applications. However, previous studies focused on the forearm or hand, whereas the wrist is more comfortable for practical use. Covering a larger ar...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2023, Vol.72, p.1-11
Main Authors: Qiu, Haomeng, Chen, Yan, Chen, Zhitao, Yang, Chaojie, Wu, Sihan, Li, Fanglin, Xie, Longhan
Format: Article
Language:English
Subjects:
Authentication
Biometrics
Biometrics (access control)
Channels
Codes
Dynamic stability
Electrodes
Electromyography
Error correction
Feasibility
Forearm
Gesture recognition
Health care
high-density surface electromyography (HD-sEMG)
pattern recognition
Prototypes
Real time
real-time authentication
Real-time systems
User experience
wearable sensors
Wearable technology
Wrist
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:In recent years, high-density surface electromyography (HD-sEMG) has been applied for cancelable authentication of wearable devices used in healthcare applications. However, previous studies focused on the forearm or hand, whereas the wrist is more comfortable for practical use. Covering a larger area with fewer electrodes (16 channels on the wrist versus 256 channels on the forearm) also reduces the chance of electrode corruption and improves the stability of the authentication system. In addition, fast dynamic gestures are more desirable than static gestures for real-time authentication. Shorter gesture execution time also makes dynamic gesture codes harder to record by imposters. We propose a cancelable real-time biometric system via wrist-worn HD-sEMG and dynamic gestures. A wearable wristband was designed to collect HD-sEMG signals. Based on a dataset with 12 dynamic gestures, we realized a fast real-time authentication. It achieved an equal error rate (EER) of 0.0039 even when imposters entered a correct gesture code. The cancelability was confirmed with an EER of 0.0018. The real-time authentication with an authentication accuracy of 96.7% and a low latency of 64 ms makes it practical in healthcare applications. These results demonstrate the feasibility of wrist-worn biometrics, which allows secure healthcare applications without compromising the user experience.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2023.3327490