## 2 Literature Review
The LMC sensor acquired hand motions and hand motion data where the pattern is sorted using a novel Chronological-Pattern Indexing (CPI) method. This approach interprets the data as a series. Hand gesture recognition systems have recently been demonstrated to offer substantial promise for use in the realm of digital entertainment. These enhancements have been made possible because of recent advancements in machine learning and sensor upgrades. Contactless hand gestures on a Leap Motion device are used to identify dynamic hand motions. This methodology is within the scope of this research study; hence, it has been included and reviewed in detail. In [16], the authors used recurrent neural networks in combination with Long Short-Term Memory (LSTM) to analyze sequential time series data acquired through the Leap Motion device for gesture recognition. The authors used both the normal unidirectional LSTM and the bidirectional LSTM. A prediction network architecture, identified as the Hybrid Bidirectional Unidirectional LSTM, is created based on the model discussed here, combined with other components. This model significantly improved performance in both forward and reverse directions by considering the spatial and temporal interactions between the Leap Motion data and the network layers. A portable DnM sign language translator is presented in [17]. This uses a controller to analyze the gestures in a photograph using various image-processing techniques. The use of deep learning has greatly enhanced the performance of this system. After completing the detection process, the hand gesture signs are converted into spoken language. There is a limitation to this application, which is also highlighted. The cost of converting this into a commercial product is high. Researchers are currently placing greater emphasis on the development of Sign Language Recognition systems that are suitable for commercial use [18]. There are several methodologies applied to find a solution to reduce costs. The main building block is data acquisition. The cost of data acquisition devices is generally high. There is a focus on finding a solution to reduce this cost, which will reduce the overall cost of a commercial product. Another aspect of the research is to review and evaluate how Sign Language Recognition (SLR) can be improved. Different Sign Language Recognition (SLR) systems being developed have their pros and cons. In [19], the authors developed a glove using flex sensors. The hand gesture data from the glove are acquired using an Arduino board. A leap motion controller is an interactive tool with many applications, such as manufacturing, 3D modeling, and other fields. This is also true in detecting hand gestures. It follows the movement of the hand with the help of cameras and infrared LEDs [20]. The device is used in combination with the SDK development toolkit, which collects hand gesture data. The results are favorable. The focus of the research in [21] is acoustic communication. The authors highlighted the difficulties that DnM people face in communicating with others. A real-time device that aids DnM people, where they use a specifically designed glove fitted with five flex sensors and one accelerometer. The sensor output will change based on hand gestures. The acquired data are then processed using an Android application. This application can interpret gestures in Arabic Sign Language (ArSL). The output is in the form of both text and voice. The prototype is a reliable and accurate device developed at a low cost. The importance of sign language is highlighted in [22]. The focus is also on NDnM people, as they also need to learn sign language so that they can communicate with DnM people. The available material is less effective in terms of training sign language for NDnM people. The authors developed an interactive Chinese sign language teaching system for smartphones that makes it easier to learn sign language. The application teaches sign language in an interactive way, where a mobile phone camera is used to record the activity.