## 5 Database  
The database of sEMG signal is collected from different volunteers for diversity purposes. All of the volunteers are able-bodied with no health issues. Each user recorded the signals at multiple sessions of the same biometric identity to allow for genuine attempts. The Myo bracelet was used to gather the data of users sEMG signals that forms a password. Each user has been asked to select three gestures out of four gestures and arrange them in a way to form a password using hand actions. A database of fifty-six participants has been collected (twenty-four males and thirty-two females with ages ranging from 16 to 62 years). The first step is to connect the armband wirelessly to the PC. Then, A software is developed to connect the Myo armband to the PC and visualize the data during data acquisition phase. The recorded data are stored into a matrix data format. The features are extracted from the collected database. The extracted features are used to train and test the offline classifiers using numerical tools. There are three different phases data collection, data processing, and feature extraction [34; 35]. Figure 2: Raw time domain surface electromyogram (sEMG) signals. To ensure that the data collected from the users are consistent, a set of instructions are prepared to apply them for all users as a data collection protocol. The users were instructed to adjust their elbow joint at an angle of 90\({}^{\circ}\) during the data acquisition. Each volunteer collected the dataset that forms the biometrics password in several sessions in order to ensure that the user can perform the same pattern that consists of a combination of hand gestures. One of the most important instructions is that the Myo bracelet has to be attached at the same position on the forearm of all users, with sensor number 4 placed on brachioradialis muscle, as shown in Figure 3. The users have the option to select three gestures from four hand gestures (Fist the hand, open the fingers, Wave-out, and wave-in). The users were instructed to adjust their hand to the resting position initially and then perform one of the proposed combination of hand gestures that forms a biometric password of this user. Subsequently, return the hand back to the initial resting position. The time-elapsed for hand gestures action is around nine seconds. Each particular user has to set a user name that will be used for log in and it is case sensitive. All of the users received a training session prior to the signature acquisition. For each user enrolled in the system, twenty tests following the protocol have been acquired. The characteristics of the database have a significant impact on the outcome of the evaluation. The amount of data available that could be used to characterize the features being compared is what determines the adequacy of the biometrics performance later on. All of the users gave their informed consent for inclusion prior to their participation in this experimental study. The research was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Ethics Committee with reference (F190916-02). The training phase of the system consists of creating training set for each user, feature extraction, and training of classifiers. There are users with 20 tests for each user. In total of 1120 tests, each test contains eight signals as a multi-channel wearable armband used to acquire EMG signals. A single binary-class classifier is trained for each user, resulting in two class outputs (access granted, or access rejected) and 56 models have been created, one classifier model for each enrolled user. As a random choice, 70% of the data of each user was selected for the training phase, which leads to 14 signals for granted class and 770 signals for rejected class, making the data highly unbalanced. Under-sampling is performed in order to overcome this problem.