Acceso usuarios
Riobamba, Ecuador
Granada, España
El uso del gesto, sea manual, facial y postural, establece una forma de comunicación hombre-máquina que aún debe ser estudiada profunda y ampliamente. Esta investigación establece la factibilidad del reconocimiento de los gestos o posturas de la mano usando un wearable (brazalete MYO) para la captura las señales electromiográficas (EMG) producidas por los músculos del antebrazo al formar un gesto. La información es capturada usando electrodos de superficie para luego, mediante el uso de clasificadores, lograr su reconocimiento. Participaron veintiún voluntarios y se analizaron doscientos setenta y tres gestos. Se evaluaron dos clasificadores, a saber, k-Nearest Neighbor (k-NN) y Support Vector Machines (SVM). El clasificador basado en SVM con núcleos Radial (93,03%) y Polinomial (97,81%) logró los mejores resultados. Además, se implementó un diccionario gestual que puede ser usado en investigaciones similares especialmente para tareas de control e interacción hombre-máquina.
The use of the gesture using hands, face, and body positions, establish a form of man-machine communication that has yet to be studied deeply and widely.
The purpose of this paper is to illustrate the feasibility of gestural recognition performed with the hand by using a wearable (MYO), which captures the electromyographic (EMG) signals produced by the forearm´s muscles, precisely by forming and maintaining the gesture. The EMG signals are captured using surface electrodes and applied in classifier algorithms to achieve gesture recognition.
Twenty-one volunteers participated in this research and two hundred and seventythree gestures were analyzed. Two classifiers were evaluated, namely k-Nearest Neighbor (k-NN) and Support Vector Machines (SVM). SVM-based classifier with Polynomial kernel (97.81%) and Radial kernel (93.03) achieved the best results.
A gestural dictionary of hand poses was implemented that can be used for similar research, especially in human-machine control tasks interaction.
