A microprocessor system for multifunctional control of upper-limb prostheses via myoelectric signal identification

• Published in: IEEE transactions on automatic control Vol. 23; no. 4; pp. 538 - 544
• Main Authors: Graupe, D., Magnussen, J., Beex, A.
• Format: Journal Article
• Language: English
• Published: IEEE 01.08.1978
• Subjects: Artificial limbs; Control systems; Electrodes; Electromyography; Filtering; Microprocessors; Muscles; Prosthetics; Signal processing; Testing
• ISSN: 0018-9286; 1558-2523
• DOI: 10.1109/TAC.1978.1101783

The paper deals with the problem of controlling artificial limbs in cases where several limb functions require control. These situations are of importance, especially to above-elbow upper-extremity amputees. Here, classical EMG (myoelectric) controllers have failed in the past, since they were based on only determining existence or nonexistence of an EMG signal. Recent work by Lyman et al. at UCLA has approached this multifunctional (MF) control problem via using a large number of electrodes, though still considering only a limited part of the EMG spectrum. The present approach is based on earlier work by Graupe et al. [4], considering the whole spectrum of the EMG signal via identifying its time series model such that several limb functions can be controlled from a single signal site. However, the present work subsequently employs parallel filtering to discriminate between the various limb functions of interest to achieve fast discrimination and control as required for practical applications, since this allows the identification itself to be performed off line. Real-time on-line results are presented in the paper, as recently obtained from tests carried out on a 1969 Vietnam above-elbow amputee who had most severe (90 percent) nerve and muscle loss at his stump, and could therefore not use more than one or two electrode pairs. The results, where complete discrimination was achieved within 0.15 to 0.2 seconds using 8-bit Intel 8080 microprocessors at double precision (incorporating hardware multipliers), have yielded an 85 percent success rate in discrimination between four to five limb functions using a single electrode pair. It is noted that the amputee mentioned had no previous EMG actuation training whatsoever.