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J. G. Hincapie and R. F. Kirsch, “Feasibility of EMGBased Neural Network Controller for an Upper Extremity Neuroprosthesis,” IEEE Transactions Biomedical Engineering, Vol. 17, No. 1, 2009, pp. 80-90.

has been cited by the following article:

• TITLE: Decoding Arm Movements by Myoelectric Signal and Artificial Neural Networks

  AUTHORS: Alexandre Balbinot, Adalberto Schuck Júnior, Gabriela Winkler Favieiro

  KEYWORDS: Surface Electromyography (sEMG); Artificial Neural Network (ANN); Assistive Technology; Prosthesis

  JOURNAL NAME: Intelligent Control and Automation, Vol.4 No.1, February 18, 2013

  ABSTRACT: The scientific researches in the field of rehabilitation engineering are increasingly providing mechanisms in order to help people with disability to perform simple tasks of day-to-day. Several studies have been carried out highlighting the advantages of using muscle signal in order to control rehabilitation devices, such as experimental prostheses. This paper presents a study investigating the use of forearm surface electromyography (sEMG) signals for classification of several movements of the arm using just three pairs of surface electrodes located in strategic places. The classification is done by an artificial neural network to process signal features to recognize performed movements. The average accuracy reached for the classification of six different movements was 68% - 88%.