Abstract
Electrical biosignals have the potential for use as biometric authenticators, owing to their ability to facilitate liveness detection and concealed nature. In this work, the viability of using surface electromyogram (sEMG) as a biometric modality for users verification is investigated. A database of multi-channel sEMG signals is created using a wearable armband from able-bodied users. Each user used his/her muscles to form a password that consists of a unique combination of specific hand gestures. A total of 18 features are extracted from the signals in order to distinguish between the users. Several features are extracted in the frequency domain after estimating the power spectral density while using the Welch’s method. Specifically, average frequency, signal power, median frequency, Kurtosis, Deciles, coefficient of dissymmetry, and the peak frequency of the sEMG signal are considered. To further increase the accuracy of the classifier, time domain features are also extracted through segmentation of the signal into 10 segments, and then calculating both the root mean square and length of the signal. Several classifiers that are based on K-nearest Neighbors (KNN), Linear Discernment Analysis (LDA), and Ensemble of Classifiers are constructed, trained, and statistically compared, resulting in an average accuracy in 97.4%, 98.3%, and 98.5%, respectively. False acceptance rate (FAR) and False Rejection Rate (FRR) are estimated for each classifier in order to determine the effectiveness of the biometrics verification system. Although the ensemble classifier accuracy was found to be the highest, the results show that the KNN classifier exhibits a FAR of 0.2% and FRR of 2.9%. Thus, the KNN classifier was found to he the optimum classifier after the extraction of all 18 features. This work demonstrates the usefulness of sEMG as a biometric authenticator in user verification.
Highlights
Wearable electronic devices have found significant applications in both the research and commercial operations of biometrics and biomedical engineering over the past decades [1].The expanding use of wearable devices and their popularity is leading to novel approaches that enhance the different ways humans interact with their environment, smart devices, and each other [2,3].The key advantage of wearables utilizing a wide variety of sensors is their ability to capture the user’s physiological and behavioural data, which can be subsequently used for biometric systems in order to verify the individual’s identity
The results presented in this paper showed that the K-nearest Neighbors (KNN) classifier exhibits a testing accuracy of 97.4%
The main aim of applying three classifier models is to select the model that is best suited for biometrics user verification
Summary
Wearable electronic devices have found significant applications in both the research and commercial operations of biometrics and biomedical engineering over the past decades [1].The expanding use of wearable devices and their popularity is leading to novel approaches that enhance the different ways humans interact with their environment, smart devices, and each other [2,3].The key advantage of wearables utilizing a wide variety of sensors is their ability to capture the user’s physiological and behavioural data, which can be subsequently used for biometric systems in order to verify the individual’s identity. Wearable electronic devices have found significant applications in both the research and commercial operations of biometrics and biomedical engineering over the past decades [1]. The expanding use of wearable devices and their popularity is leading to novel approaches that enhance the different ways humans interact with their environment, smart devices, and each other [2,3]. The key advantage of wearables utilizing a wide variety of sensors is their ability to capture the user’s physiological and behavioural data, which can be subsequently used for biometric systems in order to verify the individual’s identity. Traditional biometrics of identification/authentication include identifier (ID), password (PW), and/or ownership-based ID card methods.
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