# Reducing the Number of Sensors in the Data Glove for Recognition of Static Hand Gestures
## 2 Related Work  
Analyzing the human hand as a mechanical system, there are a total of 27 degrees of freedom [8]. Considering individual human motoric coupling, the human hand is a very complex mechanical manipulator. In addition to the complex muscular and skeletal system, the hand (apart from the central nervous system), is the most innervated part of the human body with a significant cortical representation. The human brain devotes considerable resources to processing the signals of the sense of touch. This is a crucial and complex task, to the extent that it requires the synchronous use of both hemispheres of the brain. This is due to the need to provide feedback for all hand actions [9]. In [10], the authors reviewed numerous articles published in the past year about the data glove, focusing on their capabilities in recognizing sign language (SL) signs or gestures. It is important to point out the differences between SL signs, which can be divided in terms of the dynamics of a given gesture or sign. Static signs are presented in a sequence, and there is a natural suspension point in each sign. Dynamic gestures, on the other hand, are challenging due to the lack of such a point. In addition, with SL, quite like speech, the spacing between each sign is important so that it can be understood. Differences also occur at the level of semantics. In the case of SL, all its signs have a separate and independent meaning. Gestures can also be an independent form of communication, but there is a specific group of them whose role is only to reinforce another form of communication. Additionally, there is a group of gestures that have a purely mechanical function. The documented designs of the data gloves can be divided into two main groups according to the principle of measurement (see Figure 1). The devices with indirect measurement method use a medium to read the position of the hand, and devices using the direct method are attached to it and thus track its movements. Figure 1: Classification of hand movement measuring devices according to their principle of operation. Indirect measurement methods include electromagnetic waves and sound waves [8; 9; 11]. The most popular indirect method is the one that uses cameras to track a hand using machine vision techniques. A popular device of this type is Ultraleap's Leap Motion, recently used to recognize 100 Arabic Sign Language gestures [11]. However, image-based hand tracking is subject to limitations due to the measurement method itself. The camera cannot capture movements that take place outside its field of view and those that are obscured by another object. Many everyday gestures or hand movements such as shaking hands may cause the skeleton tracking algorithm to be unable to map the position of the hand. This problem does not occur with hand tracking using the data glove [12; 13]. The second group of devices are those that use sensors placed directly on the hand. These devices are called data gloves because of their glove-like shape. They can be classified according to the type of sensors used. One of the most popular types of sensors, probably due to its relatively low price and ease of application, are piezoresistive sensors [10]. [PERSON] et al. [14], using a self-built glove, investigated the behavior of the hand while holding seven everyday objects in the palm of the hand and made a successful attempt to classify these grasps based on the collected data. [PERSON] et al. [15] conducted an experiment using a similar glove and tested the effectiveness of the device in recognizing 27 gestures and compared several methods for their segmentation and classification. Each glove has 10 deflection sensors and one Inertial Measurement Unit (IMU) for tracking hand orientation. The authors also conducted an experiment using a publicly available database containing readings obtained from a similar glove while performing sign language gestures. In both experiments, the team obtained high classification accuracy of 90% for most of the tested methods. [PERSON] et al.