## 2 Related Work  
Hand gesture recognition contributes to improving the development of human-computer interaction (HCI) strategies. There are many studies related to hand gestures using wearable and non-wearable sensors. We explain a brief discussion of that work in this section. [PERSON] et al. proposed an inertial and EMG sensor-based American Sign Language (ASL) recognition system [3]. The feature selection strategy was applied for fusing information and classified using the selected classification model. They tested 80 signs and achieved 96.16% and 85.24% accuracy for intra-class and its cross-session evaluation, respectively. In [4], the author proposed an adaptive segmentation technique for Chinese sign language recognition using EMG sensors. Moreover, the use of human-computer interaction (HCI) in terms of the recognition of sign gesture has increased through EMG signals [5; 6]. However, the noise of the input signal data may be a concern for the recognition of signs. Researchers did not apply any filtering methods, fed the acquired signal directly into the system, and achieved less accuracy compared to the trained system. A wearable device is used to recognize an Arab sign language developed using a modified k-NN method for forty sentences with an eighty-word lexicon [7]. An SVM classifier-based sign interpretation system was designed and implemented using smart wearable hand devices [8]. This system utilized flex sensors, pressure sensors, and three-in-one-speed sensors for distinguishing the ASL alphabet. However, wearable technology is uncomfortable for daily life, until there is great progress. In [9], the author presented an ASL alphabet recognition system based on multi-view augmentation and inference fusion using CNN. This method retrieved 3D data from a depth image and created more perspective views for effective training and reduced overfitting. However, the diversity of the image could not allow recognizing the real-time gesture of a specific sign. [PERSON] et al. proposed an ASL recognition system using the Kinect sensor [10]. They achieved better results than single data systems, compared to RGB images, depth images, and performance on both in this system. [PERSON] et al. also introduced the Kinect sensor for HCI [11]. The area of hand and fingertips was identified with contour extremes and palm position, and the gesture of the hand was recognized by measuring skeletal data. In [12], the author provided an invariant framework that was able to detect the occluded gestures. The work dealt with a hand gesture recognition system using Kinect sensor skeleton data. However, there may still be concerns about distance, and it is not clear how the \"no gesture\" situation was recognized by the presented algorithm. In [13], the Kinect sensing device was used to perform the comprehensive application of continuous activity recognition, and it determined the sequence of activities from 3D skeleton information. However, the authors did not explain how these were conducted during the tracking activity. [PERSON] et al. proposed Japanese and English character input systems based on the hand tapping of gestures [14]. However, the system required large computational time, and the input characters were hard to remember by the user's hand tapping gestures. In [15], the author presented a 3D motion sensor-based ASL recognition system. They used k-NN and SVM to classify the 26 letters of the English alphabet in ASL derived from the sensory data. Based on shape and texture characteristics, the LBG (Linde-Buzo-Gray) vector quantization was applied to solve the SLR system [16]. However, the author used RGB information for recognition of sign language in poor lighting; therefore, these methods failed. Meanwhile, a direct method was used to detect the gestures in the color of RGB space. For instance, the preprocessing and recognition accuracy was improved in an incoherent environment with skin-colored objects [17].