# Hand Posture Recognition Using Skeletal Data and Distance Descriptor
## 2 Related Work  
One of the devices that can be used to obtain skeletal data for hands is the Leap Motion (LM) sensor [11]. In literature, there are several works devoted to the study of its usefulness for gesture recognition. In [12], the authors estimated the accuracy and repeatability of hand position measurement and found that this sensor outperforms competitive solutions with a similar price range. In [13], the sensor's usefulness for recognizing Australian Sign Language was assessed. Hand shapes for which the sensor does not work were identified. The authors concluded that the solution has great potential, but requires refining the API. In [14], the usefulness of the device for hand tracking was assessed. The authors noticed that further development of the sensor is needed to implement professional systems. It is expected that sensors for the acquisition of hands skeletal data will be improved soon. Therefore, work is underway to use them to recognize sign languages: American [15; 16; 17; 18; 19; 20; 21; 22; 23; 24], Arabic [25; 26; 27; 28], Australian [13], Indian [29; 30; 31], Mexican [32], Pakistani [33], and Polish [34]. In [15], a subset of hand shapes from the American Finger Alphabet was recognized using features based on skeletal data: angle, distance, and elevation of the fingertips. The support vector machine (SVM) classifier was used. The recognition rate was 80.88%. After adding features obtained with the Kinect sensor--curvature and correlation--recognition efficiency increased to 91.28%. The 26 letters of the American Finger Alphabet, shown by two people, were also recognized in [16]. The feature vector consisted of pinch strength, grab strength, average distance, spread and tri-spread between fingertips, determined from skeletal data. The recognition rate was 72.78% for the nearest neighbor (kNN) classifier and 79.83% for SVM. In [25], 28 letters of the Arabic Finger Alphabet were recognized using 12 of 23 values measured by the LM sensor: finger length; finger width; average tip position with respect to \(x\), \(y\), and \(z\)-axis; hand sphere radius; palm position with respect to \(x\), \(y\), and \(z\)-axis; hand pitch, roll, and yaw. A 98% recognition rate was obtained for the Naive Bayes classifier, while it was 99% for Multilayer Perceptron. Ten static hand shapes shown by 14 users were recognized in [18]. The following features were used: fingertip angles, distances, elevations, and positions. For the SVM classifier, the recognition rate was 81.5%. After adding features obtained from the Kinect's depth image, the recognition rate increased to 96.5%. The Indian Finger Alphabet letters from A to \(Z\) and the numbers from 1 to 9, shown by ten users, were recognized in [29]. The feature vector consisted of the following distances: the fingertips--the middle of the palm, the index--the middle finger, the index--the ring finger, and the index--the little finger. For the kNN classifier, a recognition efficiency of 88.39% for the Euclidean metric and 90.32% for the cosine distance was obtained. In [28], two LM controllers were used to prevent the individual fingers from being obstructed. The 28 letters of Arabic Finger Alphabet were recognized. The feature vector was a concatenation of the following features measured by two controllers: finger length; finger width; average tip position with respect to \(x\), \(y\), and \(z\)-axis; hand sphere radius; palm position with respect to \(x\), \(y\), and \(z\)-axis; hand pitch, roll, and yaw. The Linear Discriminant Analysis classifier was used. The recognition rate of 97.7% for a fusion of features and 97.1% for a fusion of classifiers was obtained. Ten static hand shapes that can be used for rehabilitation after cerebral palsy were recognized in [35]. Features determined by the LM controller and three classification methods; decision tree, kNN, and SVM, were used. The obtained recognition rates of individual gestures ranged from 76.96% to 100%. In [20], 26 letters of the American Finger Alphabet were recognized.