Rancang Bangun Interpreter Bahasa Isyarat Indonesia menggunakan Leap Motion dan Algoritma Naive Bayes dengan Bahasa Pemrograman Python

Hasil dari penelitian ini adalah dihasilkannya sebuah alat penerjemah gerakan tuna rungu kedalam Bahasa Indonesia dengan memanfaatkan sensor leap motion sebagai penangkap vektor koordinat suatu gerakan tangan. Dari hasil pengujian yang dilakukan oleh peneliti alat ini berfungsi dengan baik dan mampu mengatasi permasalahan yang selama ini dialam

Interfaces gestuais baseados no controlador Leap Motion

In the present, most of the human-machine interactions are based on the use of peripherals such as keyboard and computer mouse. However, the use of such peripherals can create certain limitations in the way people interact with machines, for this reason, there is a need to create natural interfaces. One of the possible approaches that has been proposed involves performing gestures that are recognized by a sensor and interpreted by the computer. The use of hands on a human-machine interface is justified by the fact that the hands are an important element in nonverbal communications. Due to this, in this project several possible gesture interfaces were analyzed, using the Leap Motion sensor. The project was based on the development of methods that allowed the recognition of gestures and their association to an action that the computer should perform. Through the analysis of existing studies in the area and the various methods used to allow a program to classify a data set, a gesture classification system was developed. The classification system has tested to verify its accuracy and precision. Using the knowledge obtained throughout the project, and as proof of concept, an application was developed to demonstrate the usefulness of the classification system in a real situation. This application can recognize a gesture and associate it with a keyboard key, allowing a user to write the message resulting from the gestures he makes. This project main conclusion was that the gesture classification system trained using SVM can make a good separation of the various gestures and with this classify correctly the gestures. Most of problems that arise during the recognition of a gesture are a consequence of the Leap Motion not being able to track correctly the gesture being made