Sign Language Recognition Based on Intelligent Glove Using Machine Learning Techniques
Paúl D. Rosero-Montalvo, Pamela E. Godoy-Trujillo, Edison Flores-Bosmediano, Jorge Carrascal, S. Otero, Henry Benítez-Pereira, Diego Hernán Peluffo-Ordóñez · 2018
We present an intelligent electronic glove system able to detect numbers of sign language in order to automate the process of communication between a deaf-mute person and others. This is done by translating the hands move sign language into an oral language. The system is inside to a glove with flex sensors in each finger that we are used to collect data that are analyzed through a methodology involving the following stages: (i) Data balancing with the Kennard-Stone (KS), (ii) Comparison of prototypes selection between CHC evolutionary Algorithm and Decremental Reduction Optimization Procedure 3 (DROP3) to define the best one. Subsequently, the K-Nearest Neighbors (kNN) as classifier (iii) is implemented. As a result, the amount of data reduced from stage (i) from storage within the system is 98%. Also, a classification performance of 85% is achieved with CHC evolutionary algorithm.