# Reducing the Number of Sensors in the Data Glove for Recognition of Static Hand Gestures
## 2 Related Work
[16], on the other hand, presented a comparison of several methods for recognizing 22 static French sign language gestures. Data obtained from a built glove, employing five IMUs, were classified with the use of algorithms based on Random Forests, Naive Bayes Classifier and Multi-Layer Perceptron. [PERSON] et al. [17] proposed a glove that is also based on piezoresistive sensors but in combination with an electromyograph and inertial sensors placed on the glove. The effectiveness of the solution was tested on 10 gestures, of which 5 were dynamic gestures, and achieved a recognition rate of 89.3% for static gestures and 76.6% for dynamic gestures. Significant advances have also been made in the field of sensors themselves. [PERSON] et al. [18] have developed a method for fabricating triboelectric nanogenerators for textronic applications. The nanogenerators are highly flexible and can be freely trimmed. Their electrical properties allow them to be used as active deflection sensors. Such a potential application was tested by measuring the voltage signals generated by the sensors when individual fingers were bent. A preliminary analysis of the obtained results shows the potential validity of this solution in data glove-type devices. A similar method and results were also reported in [19]. [PERSON] et al. [20] presented sensors using graphene technology. A layer of reduced graphene oxide (RGO) was sputtered onto a piece of double-covered yarn (DCY) giving it piezoresistive properties. The performance of the sensors was tested on a set of 10 gestures representing digits from 0 to 9 and 9 dynamic Chinese Sign Language characters using artificial neural networks (ANN) [21] and dynamic time warping (DTW) method. The achieved recognition performance was 98.3%. A different method of tracking finger movement was presented by [PERSON] et al. [22]. Coils were mounted on the fingertips of the glove, emitting an electromagnetic field at a different frequency for each of the seven transmitters. To function, such a solution needs receivers placed in three planes. The developers presented a wall made for this purpose, in whose space a gloved hand moves. This approach precludes the use of the device in open spaces which is one of the main drawbacks of using vision-based methods for gesture recognition. In [23], a method of applying the sensors directly to the surface of the material is presented. The resistive fingertip deflection sensors on which the glove performance is based were fabricated using the direct indium-gallium eutectic writing method. The software part of gesture recognition problem can be divided into two parts: detection and classification. While neural networks and machine learning algorithms can often achieve high classification performance, the need to capture the beginning and end moments of a given gesture poses a significant problem for devices serving as SL translators. Many systems developed to date have not been able to recognize dynamic SL characters, and none have operated in real time outside of laboratory environment. This is due to the difficulty of segmenting readings from such a glove. Separating sign language characters from each other is still a difficult challenge for the currently developed algorithms [10]. An original segmentation approach that is based on a scalar called Gesture Progression Scalar (GPS) was proposed by [PERSON] and [PERSON] [23]. The GPS is determined by the dynamics of resistance changes in 10 sensors. Based on its values, it is possible to determine the moment at which a given gesture starts and ends. It is also worth mentioning that both presented algorithms were tested on a dedicated glove without any inertial sensor, which is a typical approach for dynamic gesture recognition. The ANN-based recognition algorithm was tested on 10 American SL characters. The final step in the gesture recognition pipeline is gesture classification. Machine learning algorithms are often used for gesture classification.