EMG Biometric Systems Based on Different Wrist-Hand Movements

Abstract

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.

Keywords

• Electromyogram (EMG)
• wrist-hand gestures
• wearable device
• biometric verification system
• biometric identification system