Abstract—The paper focuses on a wireless myoelectric
prosthesis of the upper-limb that uses a Multilayer Perceptron
(MLP) neural network with back propagation algorithm in
classifying electromyography (EMG) signals. MLP Neural
network is composed of processing units that have the capability
of sending signals to each other and perform a desired function.
The algorithm is widely used in pattern recognition. The
network is used to train EMG signals and use it in performing
the necessary hand positions of the prosthesis. Through
programming a Field Programmable Gate Array (FPGA) using
Verilog and transmission of data with Zigbee, the EMG signals
are acquired, classified, and simulated wirelessly. The signals
are classified and trained to produce the necessary hand
movements. The corresponding hand movements of Open, Pick,
Hold and Grip are simulated through the Zigbee controller.
Z-test is used to analyze the data that were produced and
acquired from using the neural network.
Index Terms—Field programmable gate array, multilayer perceptron neural network, verilog, ZigBee.
K. D. Manalo is with Mapúa Institute of Technology, Manila, Philippines (tel.:+639367473784; e-mail: email@example.com).
J. L. Torres and N. B. Linsangan are with Mapúa Institute of Technology, Manila, Philippines (e-mail: firstname.lastname@example.org, email@example.com).
Cite: Kevin D. Manalo, Noel B. Linsangan, and Jumelyn L. Torres, "Classification of Myoelectric Signals Using Multilayer Perceptron Neural Network with Back Propagation Algorithm in a Wireless Surface Myoelectric Prosthesis," International Journal of Information and Education Technology vol. 6, no. 9, pp. 686-690, 2016.