Development of a Myoelectric-Controlled Prosthesis for Transradial Amputees




prosthesis, electromyography, bayesian classifier, artificial neural network


In this paper, the development and operation of a robotic prosthesis for transradial amputees is presented. This prosthesis consists in a 3D-printed prototype with two degrees of freedom, allowing the user to perform grip tasks and to orientate objects through pronation and supination movements. Two classifiers were used independently to control the prosthesis: a bayesian classifier implemented in an Arduino device and an artificial neural network implemented in MATLAB® software; both classify movements through the acquisition, processing and extraction of features from the electromyography signal. The bayesian classifier and the artificial neural network achieved an efficiency of 97% and 100%, respectively, which shows that the extracted features were suitable for the proposed electromyography classification. A completely functional 3D-printed myoelectric prosthesis was achieved, and it represents a low-cost alternative to those existent in the current market.


Download data is not yet available.



How to Cite

Rodríguez-García, M. E., Dorantes-Méndez, G., & Mendoza Gutiérrez, M. O. (2017). Development of a Myoelectric-Controlled Prosthesis for Transradial Amputees. Revista Mexicana De Ingenieria Biomedica, 38(3), 602–620.



Special Issue

Dimensions Citation