Cybernetic Robotics – ARAS (Advanced Robotics and Automated System)

In the physical sciences, neuro cybernetics is the study of communication and automatic control systems in mutual relation to machines and living organisms. The underlying mathematical descriptions are control theory, extended for complex systems, and mean field theory for neural networks and neural field theory. Exemplary applications of walking and human arm control and further reading can be found here. Neuro cybernetics is a sub-discipline of biocybernetics. In this research neuro-cybernetic signals (EMG) are aquired and classified in order to command artificial arm prosthesis.

EMG signal extraction and feature selection

EMG

Electromyography (EMG) is a technique for evaluating and recording the activation signal of muscles. EMG is performed using an instrument called an electromyograph, to produce a record called an electromyogram. An electromyograph detects the electrical potential generated by muscle cells when these cells are both mechanically active and at rest. The signals can be analyzed in order to detect medical abnormalities or analyze the biomechanics of human or animal movement.

A real-time application of artificial neural network is to classify EMG signal in different arm motion. The neural network output represents a degree of desired muscle stimulation over a synergic, but enervated muscle. By means of this procedure, the network can learn to map a set of inputs to a set of outputs. The experimental result on the ANN classification method is given in the following table, as it is seen in this table the developed routine can classify the motion quite accurately with less than %4 error.

EMG signal classification

	Flexion	Extesion	Supination	Pronation	Rest	% Error
Flexion	211	0	5	0	0	2.31
Extesion	0	216	0	0	0	0
Supination	4	0	212	0	0	1.85
Pronation	0	0	0	210	6	2.78
Rest	0	0	0	7	209	3.24

Saeed Mahyad, Soheil Kianzad, Zahra Marvi, Sedighe Dehghani, Golnoosh Hosseini, Hoda Akbari, Mahboobeh Malekdoost, Zinab Khorasani.

Force Control of Intelligent Laparoscopic Forceps, Soheil Kianzad, Soheil O. Karkouti, and Hamid D. Taghirad, Journal of Medical Imaging and Health Informatics, Vol. 1, 284–289, 2011

Self-tuning dynamic impedance control for human arm motion, S Dehghani, HD Taghirad, M Darainy, Biomedical Engineering (ICBME), 2010 17th Iranian Conference of, 2010.

Neuro-musceleton model of below-elbow-arm and a new control topology for its cybernetic prosthesis, (pdf), H.D. Taghirad and S. Mahyad, 13^thInt. Iranian Conference on Electrical Engineering, Vol 3. pp183-188, Zanjan.