# Gesture Recognition of Sign Language Alphabet Using a Magnetic Positioning System
###### Abstract  
Hand gesture recognition is a crucial task for the automated translation of sign language, which enables communication for the deaf. This work proposes the usage of a magnetic positioning system for recognizing the static gestures associated with the sign language alphabet. In particular, a magnetic positioning system, which is comprised of several wearable transmitting nodes, measures the 3D position and orientation of the fingers within an operating volume of about 30 \(\times\) 30 \(\times\) 30 cm, where receiving nodes are placed at known positions. Measured position data are then processed by a machine learning classification algorithm. The proposed system and classification method are validated by experimental tests. Results show that the proposed approach has good generalization properties and provides a classification accuracy of approximately 97% on 24 alphabet letters. Thus, the feasibility of the proposed gesture recognition system for the task of automated translation of the sign language alphabet for fingerspelling is proven. gesture recognition; sign language recognition; fingerspelling; hand tracking; magnetic positioning system; machine learning; wearable electronics +
Footnote †: journal: Medicalas fingerspelling, is limited to a collection of manual signs that represent the symbols of the letters of an alphabet using one hand [5]. The American Sign Language (ASL) signs corresponding to the letters of the alphabet are shown in Figure 1.  
Sign language recognition (SLR) is a multidisciplinary research area that involves natural language processing, linguistics, pattern matching, computer vision and machine learning [6]. The final goal of sign language recognition is to develop methods and algorithms in order to build a sign language recognition system (SLRS) capable of identifying already produced signs, decoding their meaning and producing some sort of textual, audio or visual output in another language that the intended receiver can understand. The problem of SLR by automatic systems can be considered as a special case of Natural Language Processing (NLP) or as a specific application of gesture recognition, which is included within the broad domain of Human Computer Interaction (HCI). In this sense, SLRS are considered as a special group of HCI devices, designed to enable effective interaction with an SL speaker. Therefore, research in sign language recognition is highly influenced by hand gesture recognition and HCI research. A search for topics like \"gesture recognition\", \"hands tracking\", \"finger tracking\", and \"sign language recognition\" on an  
Figure 1: Letters of the American Sign Language (ASL) alphabet. online scientific library may return over 100k results for each query, with a rising trend and a peak in a number of published papers in the period 2016-2020. In the context of hand gesture recognition, many common devices and applications rely on the tracking of hands, fingers or handheld objects. Specifically, smartphones and smartwatches track 2D finger position, a mouse tracks 2D hand position, and augmented reality devices like the Microsoft HoloLens 2 track the 3D pose of the finger. In addition to SLR, other applications of hand gesture recognition include virtual reality, augmented reality [7], assistive technology [8; 9], collaborative robotics [10], tele-robotics [11], home automation [12], infotainment systems [13; 14], intelligence and espionage [15] and many others [16]. The general SLR problem includes the following three different tasks:  
1. Static or continuous letter/number gesture recognition (classification problem);
2. Static or continuous single word recognition (classification problem);
3. Sentence-level sign language recognition (NLP problem). Available literature surveys [16; 4; 17; 18] report that recent research achieved an accuracy in the range of 80-100% for the first two tasks, when some given specific conditions are met.