# Gesture Recognition of Sign Language Alphabet Using a Magnetic Positioning System
###### Abstract
In this paper, we focus on the recognition of static hand gestures associated with the letters of the alphabet for fingerspelling, which is part of Task 1. A similar task was also addressed by many other works, such as [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29; 30; 31; 32; 33; 34; 35], using different approaches for realizing the software and hardware components of the proposed systems. Both computer-vision-based and sensor-based approaches have been implemented for sign language alphabet recognition. The vision-based approaches [19; 20; 21; 22; 23; 24; 25; 26; 27; 28; 29] use RGB cameras or depth cameras (such as the Leap Motion controller or the Kinect) to capture the image or the video frames of the hand-performing signs. These frames are further processed to recognize the signs and produce some text or speech output. Hand features extraction is a major challenge for the vision-based systems: extraction is affected by many factors, such as lighting condition, complex backgrounds in the image, occlusion, and skin color. Sensor-based gesture recognition systems are commonly implemented as data gloves featuring inertial measurement units and stretch sensors such as in [30; 31; 32], as surface electromyography sensors [33], or other wearable devices [34; 35]. Sensor-based approaches have the key advantage of simplifying the detection process. On the other hand, a disadvantage of sensor-based systems is that they can be expensive, cumbersome, or too invasive for real-world deployment. A fundamental observation that can be made by analyzing the state-of-the-art outlined above is that building a gesture recognizer on top of a tracking system, instead of direct classification from a sensor stream, can help make the gesture recognition system less dependent on input devices. In particular, in this paper, we demonstrate that training the classification model on data from a tracking system gives substantial advantage in terms of robustness to environmental condition and signer variability. We propose a sensor-based approach that employs a Magnetic Positioning System (MPS). The MPS is comprised of transmitting nodes and receiving nodes. The transmitting nodes are mounted on the fingers and hand to be tracked, whereas the receiving nodes are placed at known positions in the sides of the operational volume. An advantage of the proposed system, compared to the others mentioned above, is that it measures the absolute position of the fingers, thus enabling high accuracy and drift-free tracking. Another advantage is that the MPS is not sensitive to illumination conditions and the other factors affecting vision-based systems. Furthermore, it operates also in the presence of obstructions caused by objects or body parts. Therefore, the proposed approach enables robust and reliable tracking of the hand and fingers. It is thus suitable for SLR and for the other applications of hand gesture recognition, such as human-machine interaction, virtual and augmented reality, robotic telemanipulation, and automation. The remainder of this paper is organized as follows: the proposed magnetic positioning system, method for reconstructing the hand gesture, and machine learning classification models are described in Section 2. Then, experimental results are presented in Section 3. A discussion of the results is then provided in Section 4, and conclusions are drawn in Section 5.