Electromyographic control

Walbran S, Callus E, Dunlop GR, Anderson I (2009) Optimization of electrode placement in electromyographic control of dielectric elastomers. EAPAD 2009, Proc SPIE 7287 728724.1-728724.12. doi 10.1117/12.815833... 

In early 1982, the first patient-controUed ambulation for a complete paraplegic as necessary for independent ambulation was achieved by Graupe et al. [5-7] employing EMG (electromyograph) control. A manually controlled system, known as the Parastep FNS system was tested from 1982 and received FDA approval in 1994 to become the first FNS ambulation system to be so approved and to be commercially available for use by individuals beyond research environments. 

Graupe, D., Kohn, K.H., Basseas, S., and Naccarato, E. (1984), Electromyographic control of functional electric stimulation in selected paraplegics. Orthopedics, 7 1134-1138 Graupe, D. (1989), EMG pattern analysis for patient-responsive control of FES in paraplegics, IEEE Trans. Biomed. Eng., 36 711-719. 

Review of Electromyographic Control Systems Based on Pattern Recognition... 

Keywords— electromyographic control, prostheses, pattern recognition, feature extraction, classification. 

The concept of using EMG for prosthesis control started in the 1940s. Electromyographic control is when the signal is used as the input for the control of powered prostheses. 

Fig. 1 shows the block diagram presenting the relationship between normal and electromyographic control system. 

Generally, electromyographic control can be divided into two non-pattern recognition based and pattern recognition based. Non-pattern recognition based control is basically constructed using hierarchical control, threshold control, proportional control or finite state machines. Most of the... 

Fig. 1 Block diagram presenting relationship between normal and electromyographic control system (shaded area is removed by amputation) [Parker et al., 2006)...

Fig. 2 The block diagram of an electromyographic control system (ECS) based on pattern recognition...

The most right column of the table shows the accuracy of the electromyographic control systems and the success of the system is depends upon the classification accuracy. It measures the number of correct classification achieved for a number of trials. From the table, it shows that all accuracies are from 90% to 98%. 

The whole hand may be operated by electromyographic signals from two antagonistic muscles in the supporting forearm stump, picked up at the skin surface. In response to tension in one muscle, the hand opens progressively and then closes to grip with an automatic reflex. The second muscle controls the mode of operation as the hand moves between the states of touch, hold, squeeze, and release. 

Sennels, S., Biering-Soerensen, E, Anderson, O.T., and Hansen, S.D., Functional neuromuscular stimulation control by surface electromyographic signals produced by volitional activation of the same muscle adaptive removal of the muscle response from the recorded EMG-signal, IEEE Trans. Rehab. Eng. TRE-5 195 206,1997. 

Davis, R. C. (1956). Electromyographic factors in aircraft control The relation of muscular tension to performance (EF SAM Rept. 55-122). Randolph Air Force Base, TX U.S. Air Force School of Aerospace Medicine. 

Gobel, M. (1996b). Electromyographic evaluation of sensory feedback for movement control. Proceedings of theXIth International Ergonomics asidSefety... 

Svebak, S., Anjia, R., Karstad, S. I. (1993). Task-induced electromyographic activation in fibromyalgia subjects and controls. Scandinavian Journal of Rheumatology, 22, 124-130. 

Mann, R. W. (1968). Efferent and afferent control of an electromyographic proportional rate, force sensing artificial elbow with cutaneous display of joint angle. Proceedings (fthe Symposium on the Basic Problems of Prehension, Movement, and Control of Artificial Limbs, Institution of Mechanical Engineers, London, pp. 86-92. 

Zecca, M., Micera, S., Carrozza, M. C. and Dario, P. (2002) Control of multifunctional prosthetic hands by processing the electromyographic signal. Critical Rev. Biomed. Eng., 30, 459-85. 

Crawford, B., Miller, K., Shenoy, P. and Rao, R. (2005) Real-Time Classification of electromyographic signals for robotic control. Technical Report No. 2005-03-05, Department of Computer Science, University of Washington. 

Some research has been conducted on the types of electrodes to use in chronic recording and stimulating involved with electromyographic work and myoelectric control problems. Two rather different types of electrode systems have been proposed (Caldwell and Reswick, 1967 Miller and Brooks, 1971). These electrodes are shown in Figure 8.7. Wire implants have been used by Schane (1973). 

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