Electromyogram (EMG) acquisition and analysis is growing in importance with human attempts to interact with and control equipment such as robots, prostheses or virtual environments. In some cases, only approved users should be permitted these capabilities. For these applications, securing EMG-based control is a major open question - to the best of the authors’ knowledge, no prior art exists which can identify individuals from a wide range of wrist-hand gestures EMG readings within the wearable device. This paper addresses this problem. Techniques are presented which allow EMG to be used as a biometric, allowing users to verify themselves. An EMG-sensing armband attached to the lower forearm is used to anonymously authenticate users as a member of an approved group, or to identify themselves uniquely. For the development of extensive biometric system, three EMG datasets with similar EMG sensing in different sessions were exploited. For verification, accuracy of up to 93% is achieved, with 92% achieved for identification. The system is also shown to operate in real-time on an ARM Cortex A-53 embedded processor suitable for housing in an EMG wearable device, incurring latencies of 1.06 ms and 1.61 ms for verification and identification respectively. These metrics are comfortably sufficient for use in real-time, battery-powered EMG authentication devices
Bibliographical noteFunding Information:
This work was supported in a part by the Ph.D. project within School of Electronics, Electrical Engineering and Computer Science, Queen’s University Belfast, U.K., from the Engineering and Physical Sciences Research Council (EPSRC), U.K., Studentship under Grant 2015–2019.
© 2013 IEEE.
Copyright 2021 Elsevier B.V., All rights reserved.
- biometric identification system
- biometric verification system
- Electromyogram (EMG)
- wearable device
- wrist-hand gestures
ASJC Scopus subject areas
- Computer Science(all)
- Materials Science(all)