# Hand Posture Recognition Using Skeletal Data and Distance Descriptor
## 2 Related Work
The features measured by the LM controller and the Multilayer Perceptron (MLP) classifier were used. A recognition rate of 96.15% was achieved. Forty-nine static gestures (twenty-six letters of the alphabet, ten numbers, and nine words) of Indian Sign Language were recognized in [31]. The feature vector was composed of the distances between the middle of the hand and the fingertips. The kNN classifier with four different measures of similarity: Euclidean distance measure, cosine similarity, Jaccard similarity, and Dice similarity was used. For gestures performed by ten people, recognition rates ranging from 83.11% to 90% were obtained. In [27], forty-four static gestures (twenty-eight letters, ten numbers, and sixteen words) from the Arabic Sign Language were recognized. Two variants of the feature vector consisting of 85 and 70 scalar values measured by the LM controller were considered. The training set consisted of 200 performances of each gesture by two people. Tests were carried out on 200 executions of individual gestures by a third person. Three variants of the classifier were considered: SVM, kNN, and artificial neural network (ANN). The best recognition rate of 99% was obtained for the kNN classifier and the first considered feature vector variant. Twenty-six letters and ten numbers of the American Finger Alphabet were recognized in [23]. Six different combinations of the following features were considered: standard deviation of palm position, palm curvature radius, the distance between the palm center and each fingertip, and the angle and distance between two adjacent fingertips. For gestures performed by twelve people and leave-one-subject-out protocol, the recognition rate was 72.79% for SVM and 88.79% for the deep network. The 24 characters of the American Finger Alphabet shown by five people were recognized in [24]. Skeletal data in the form of angles between adjacent bones of the same finger and angles between adjacent fingers were used, as well as infrared images obtained with an LM sensor. For the classifier based on deep networks and leave-one-subject-out protocol, a recognition rate of 35.1% was obtained. In [34], 48 static hand shapes from the Polish Finger Alphabet and Polish Sign Language were recognized. Gestures were shown 500 times by five users. Two different positions of the LM sensor and changes in the orientation of the hand were considered. Several classifiers, as well as their fusion, were tested. For the leave-one-subject-out protocol, the best recognition rate was 56.7%. Twenty-four static gestures of the American Finger Alphabet, shown ten times by twelve people, were recognized in [22]. Features based on the skeletal data returned by the LM controller were used. The classification was carried out using hidden Markov models. For the leave-one-subject-out protocol, the recognition rate was 86.1%. In [21], 12 dynamic and 18 static gestures of American Sign Language, shown by 20 people, were recognized. The feature vector was composed of the following: the internal angles of the joints between distal and intermediate phalanges, and intermediate and proximal phalanges; 3D displacements of the central point of the palm; 3D displacements of the fingertip positions; and the intrafinger angles. Recursive neural networks were used for the classification. The recognition rate was 96%. The problem of fingers occlusion, occurring when performing gestures, was considered in [36]. For this purpose, three LM controllers were used. The feature vector consisted of a hand rotation matrix, \(y\)-component of the fingers directions, and distal phalanges rotation quaternion. Three classification methods available in the scikit-learn library were tested: logistic regression, SVC, and XGBClassifier. An 89.32% recognition rate was obtained for six selected hand gestures. In [37], letters from the Israeli Sign Language (ISL) alphabet were recognized using the SVM classifier and feature vector consisting of the Euclidean distances between the fingertips and the center of the palm.