IEEE TRANSACTIONS AUTOMATIC CONTROL, VOL. ON
AC-29,NO. 4, APRIL 1984
The Control of a Prosthetic Arm by EMG Pattern Recognition
Abstract -An electromyographic (EMG) signal pattern recognition system is constructed for real-lime control a prostheticarm through precise of identificationof motion and speed command. A probabiistic modelofthe EMG patterns is f i i formulatedinthe featurespace of integralabsolute d u e (IAV) to describetherelation betweena command, represented by motion and speed variables, and location and shape of the corresponding pattern. The model provides the sample probability density function of pattern classes in the decision space relations between L4V, ...view middle of the document...
Butthe extreme compIexities involved in the EMG signals make it difficult to have a precise structural or mathematical model which relates the measured signals with a motion command [l], . Three major approaches been have suggested to solve the motion command identification problem. The first, based on the works of Graupe et al. , , models the EMG signals as a stationary time series ( A R model), and the model parameters for each of the prespecilied motion classes are identified to form a reference parameter set. The measured EMG signals are classified into one of several prespecified motion classes, either by parameter vector space methods or parallel filtering methods. The second, based on the works of Wirta et ai. [ 5 ] , uses many electrode sites to form a pattern which represents the spatial distribution of the time integrated value of the EMG signals. A linear discrimiManuscriptreceivedMarch18,1982; raised March4.1983.Paper recommendedby A. K. Bejczy,PastChairman of the Automation and Robotics Committee. ih The authorsare w t theDepartment of Electrical.Computer.and Systems Engineering, Rensselaer Polytechnic Institute. Troy. NY 12181.
nant function is designed for the classification by proper assignment of weighting coefficients and summationthreshold. The third, based on the works of Saridis et a / . , , forms patterns of prespecifiedmotionclasses in thefeaturespace of variance and zero crossings, wherethe EMG signal variance and zero crossing are selected as thebestfeaturesubset in thesense of class separability by thefeaturesubset selection procedure. A learning linear classifier is designed to investigate the upper bound of misclassification for each pairwise classification of27 motion classes. Some other important works on prosthetic arm control are the reflexive and trajectory control by Lyman et a/. and Freedy et ai. . , thetask classification byLawrence et ai. [lo], and the shoulder torque analysis by Jacobsen et a/. [ll]. Also, many of theaspectsrequired for the practical success of theprosthetic arm research are well described by Jacobsen et a/. . Although previous work has brought some sort of theoretical and practical achievements for the control of a prosthetic arm, further advancement, such as the accurate identification of motion and speed command from the EMG signals and the design of a faster and more reliable command identification procedure, enough to be processed within the limitation of time and error rate, is required to achieve an ultimate goal, the anthropomorphic movement of a prosthetic arm with minimum mental effort. This paper presents a definite contribution to the attainment of the above goal by developing further the latter approach. At first, a probabilistic model of the EMG pattern is formulated based on the pattern trajectory with respect to the command variation. The modelprovides not onlythesample probabilitydensityfunction of pattern classes in thedecision space but also...