## 1 Introduction  
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. The user has a risk of forgetting the password or user name or might lose the ownership-based ID card [4; 5]. However, this risk is fully eliminated with wearable biometrics. Biometrics systems are based on recognizing user's data by using either unique physical or behavioral features of the user. The physical features include fingerprint, face recognition, and eye (iris) scan. While, the behavioral ones include gait recognition, voice recognition, Electrocardiography (ECG), Electromyography (EMG), and electroencephalogram (EEG) [6; 7]. Although biometric technology has seen significant advances, some biometric systems fail to meet security and robustness requirements in certain real-world situations. By way of an example, the susceptibility to to spoofing--persons who pretend to be others for the purpose of obtaining illegal accesses to private information or services [8; 9]. The study and prevention of spoofing is considered to be an active area of research and development. As wearable devices, utilizing sEMG can capture the detailed characteristics of the human muscles and, thus, it is useful in human gesture recognition applications. The information extracted from sEMG signals obtained via a human arm is sufficient for classifying intended hand gestures [10]. The primary objective of this work is to demonstrate the utilization of the sEMG multi-channel wearable armband in authenticating the identity of individuals with the application of Machine learning algorithms. Previous studies using ECG/EMG sensors fusion to authenticate the identity of the users can be found in [11]. [PERSON] et al. [12] studied the usefulness of a biometric system utilizing information that was obtained via both ECG and EMG physiological data. A non-intrusive one-lead ECG setup was adapted into the palm of the user to collect ECG biometric data. Subsequently, the authors used Fourier descriptors for feature extraction. Finally, an optimum-path forest classifier was used in order to distinguish between individuals. [PERSON] et al. [13] proposed a non-contact secure private authentication that is based on an EMG signals. A total of fifty signals were extracted from the arm of subjects with the assistance of a two channel electrode system. A set of metrics, such as the mean, length, variation, zero crossing, and median frequency, were extracted from the signals to enhance the identification rate and formulate a machine learning algorithm. The Artificial Neural Network (ANN) algorithm showed a relatively high accuracy of 81.6%. [PERSON] et al. [14] used vector quantization and Gaussian mixture model in order to obtain the EMG signals for use in biometric applications. The identification rate of 97.9% was achieved with an average of 73.33% obtained from 49 individuals. The aforementioned experiment demonstrated that EMG signals alone can produce user distinguishable biometric data. [PERSON] et al. [15] presented a novel pseudo-wavelet function for mechanomyographic (MMG) signal extraction during dynamic fatiguing contractions. A bio-impedance analysis (BIA) wrist band has been used to identify users with an eight-electrode installed. The success rate with BIA was 85% and, by adding a circumference with 1 mm accuracy, the authors improved their result up to 90%. [PERSON] et al.