In this study, we present the detection of the up-downward as well as the right- leftward motion of cursor based on feature extraction. In this algorithm, the K-means clustering method is used to recognize the available hidden patterns in each of the four modes (up, down, left, and right). The identification of these patterns can raise the accuracy of classification. The membership degree of each feature vector in the proposed new patterns is considered as a new feature vector corresponding to the previous feature vector and then, the cursor motion is detected using the linear SVM classifier. Applying the proposed method for data based on the hold-up cross validation causes the accuracy of the classifier in the up-downward and left- rightward movements in each person to increase by 2–10 %.

在这项研究中，我们提出了基于特征提取的光标上下左右运动的检测。在该算法中，使用K均值聚类方法来识别四种模式（上，下，左和右）中每种模式下可用的隐藏模式。这些模式的识别可以提高分类的准确性。所提出的新模式中的每个特征向量的隶属度被认为是与先前特征向量相对应的新特征向量，然后，使用线性SVM分类器来检测光标运动。将所提出的方法应用于基于交叉交叉验证的数据，会使分类器在每个人的上下左右运动中的准确性提高2-10％。