Development of Miniaturized Wearable Wristband Type Surface EMG Measurement System for Biometric Authentication

Abstract

Personal authentication systems employing biometrics are attracting increasing attention owing to their relatively high security compared to existing authentication systems. In this study, a wearable electromyogram (EMG) system that can be worn on the forearm was developed to detect EMG signals and, subsequently, apply them for personal authentication. In previous studies, wet electrodes were attached to the skin for measuring biosignals. Wet electrodes contain adhesives and conductive gels, leading to problems such as skin rash and signal-quality deterioration in long-term measurements. The miniaturized wearable EMG system developed in this study comprised flexible dry electrodes attached to the watch strap, enabling EMG measurements without additional electrodes. In addition, for accurately classifying and applying the measured signal to the personal authentication system, an optimal algorithm for classifying the EMG signals based on a multi-class support vector machine (SVM) model was implemented. The model using cubic SVM achieved the highest personal authentication rate of 87.1%. We confirmed the possibility of implementing a wearable authentication system by measuring the EMG signal and artificial intelligence analysis algorithm presented in this study.

Wearable sensors for monitoring vital signals in sports and health: progress and perspective

“…In a separate instance, the integration of flexible electrodes and signal processing circuits onto a wristband was used to categories the EMG data. The device maintained consistent contact with the skin and reduced motion noise by the application of pressure (Shin et al, 2021) EMG is a technique used to detect and analyze the minute changes in the electrical signals resulting from muscle activation. This method has found applications in the fields of sports science and neuromuscular rehabilitation.…”

“…In a separate instance, the integration of flexible electrodes and signal processing circuits onto a wristband was used to categories the EMG data. The device maintained consistent contact with the skin and reduced motion noise by the application of pressure (Shin et al , 2021). In their study, Posada-Quintero et al fabricated a strip EMG electrode by combining toner, salt and viscoelastic polymer adhesives (CSA) (Posada-Quintero et al , 2015).…”

“…The purpose of this integration was to accurately identify and classify movements. The purpose of the array is to provide geographic coverage of the skeletal muscles to collect regional sample data of EMG activity from 18 distinct motions, as depicted in Figure 12(d–e) (Shin et al , 2021). The accuracy of the system in distinguishing between distinct static and dynamic gestures is approximately 97% when a single trial per gesture is used for training.…”

Biometric Authentication Based on EMG Hand Gestures Signals Using CNN

“…The feature vector can be obtained using fast Fourier transform (FFT), discrete wavelet transform (DWT), and average frequency methods. These feature vectors can be classified by conventional methods such as support vector machines (SVM) [8], [9], multilayer perception (MLP) [8], [10], k-nearest neighbours (kNN) [11], and artificial neural network (ANN) [3].…”

A Review of Recent Advances in Vital Signals Monitoring of Sports and Health via Flexible Wearable Sensors

“…These nanoparticles improved the surface area of the electrodes, which significantly reduced interfacial impedance and thermal noise, and motion artifacts were reduced by 95%. In another case, flexible electrodes and signal processing circuits were integrated onto a wristband to classify the EMG signals [ 61 ]. The device had stable contact with the skin and minimized motion noise by applying pressure.…”