The reason for using neural-net tech- nology is simple enough: It can solve problems that conventional statistical methods cannot, at least not within acceptable ...
Myoelectric Signal Recognition using Fuzzy Clustering and Artificial Neural Networks in Real Time Adrian Del Boca and Dong C. Park Intelligent Computing Research Lab Department of Electrical and Computer Engineering Florida International University Miami, FL 33199 e-mail: parkdQcherry.fiu.edu Abstract Application of EMG-controlled functional neuromuscular stimulation to a denervated muscle depends largely on the successful discrimination of the myoelectric signal (MES) by which the subject desires to execute control over the impeded movement. This can be achieved by an adaptive and flexible interface that is robust regardless of electrode location, strength of remaining muscle activity or even personal conditions. A real-time application of artificial neural network that can accurate recognise the MES signature is proposed in this paper. MES features are first extracted through Fourier analysis and clustered using fussy c-means algorithm. Data obtained by this unsupervised learning technique are then automatically targeted and presented to a multilayer perceptron type neural network. For real-time operation, a digital signal processor operates over the resulting set of weights and maps the incoming signal to the stimulus control domain. Results show a highly accurate discrimination of the control signal over interference patterns.
1
Introduction
Functional neuromuscular stimulation (FNS) is a procedure by which paralysed or paretic muscles are electrically stimulated to provide a functional, useful movement. An important consideration in such a system, concerns the nature of the command signal required to trigger the stimulation for initiating the movement of a paralysed extremity. In providing such a command signal, one aims to bypass the lesion in some manner, thus allowing the patient to regain voluntary control over the paralysed muscles. The possibility of using the myoelectrical activity as a control signal is particularly attractive since it involves the natural and inborn body positioning system. The focus of our research aims to investigate a robust and adaptive system that extract from the patient inherent movement (e.g. synergic muscles), a control signal to trigger such a FNS device over a denervate muscle. Dynamic electromyography offers a means of directly tracking muscle activity. The myoelectric signal sufficiently parallels the intensity of muscle action to serve as a useful indicator of its mechanical activity. Its multispike, random amplitude quality, defies simple interpretation. To overcome this limitation, multiple processing and interpretive techniques have evolved. Time domain analysis for quantification of the MES by ensemble average and standard deviation of linear envelopes (LE) have shown to be valuable as a diagnostic tool in the assessment of pathological gait [9]. However, the validity of normal profiles defined by this method has been questioned because the ensemble averaged profile would not lead t o a representative profile of LE for a given muscle. Saridis et al. have developed a control system for a prosthetic arm based on a statistical analysis of the MES involving a study of sero crossings [l]. Hef€tner et al. have studied the above-lesion MESS for the control of FNS by using pure autoregressive parametric models [2 . However, the use of single channel autoregressive model to extract the features of MES linear envelope (]LE) has shown rather poor classification results [4]. Studies had concluded that general patterns of the MES LE resemble
0-7803-1901-X/94 $4.00 01994 IEEE
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the LE’S from which they inherit the phase spectra. To represent the phasic activity pattern, the LE can be expressed in terms of unnormalized Gaussian pulses [4]. Many methods have been proposed to estimate the parameters of a Gaussian function. Among them, the methods of moments (MOM) and maximum-likelihood estimation (MLE) are the most used. However, equations associated with them usually converge very slowly and occasionally to spurious maxima. Present studies show different feature extraction and clustering methods of the MES in the frequency domain. The myoelectric signal, a nonperiodic signal, pPssesses all the characteristics of a random signal and consequently can be analyzed using Fourier analysis. The electromyogram (EMG) power spectrum represents a continuous distribution of the power of the signal as a function of its frequency. For MES LE clustering approached by Fourier analysis, the number of harmonics determines the pattern vector dimension and affects the clustering results. EMG pattern classification is done by choosing an appropriate number of harmonics and classified by measuring distances intracluster and intercluster of percent power and phase angle. Based on this features, clustering was performed using K-means-DYNOC algorithms [8]. The utility of ANN has been proven to be a useful classification tool for ME signals. Early works, however, have been based on simple feature extraction or time series parameters generation through Hopfield networks which may not be suitable for a real-time application [lo]. The purpose of this research is to find a robust but yet flexible mean of MES real time recognition from noise patterns, adaptable to different individuals with a minimum system customization. This is accomplished by extracting features of the MES by Fourier analysis and clustering within a normal classification space. Fuzzy c-mean algorithm clusters the feature space into nine degrees of muscle contraction providing each MES pattern with a target value (section 2). Noise patterns from different sources were added to the classification space with a null target value. The mixed MES and noise patterns become the training data of a particular muscle for a given individual. A multi-layer perceptron based on the error-back propagation algorithm provides the weights that match an incoming MES or noise pattern into a desired output (section 3). The neural net output represents a degree of desired muscle stimulation over a synergic but denervated muscle. Environmental noise patterns, as well as the ones originated by the stimulating device itself, once learned, should not represent a stimulus to such muscle. Real time operation is achieved by taking advantage of hardware multipliers present in DSP processors to perform Fast Fourier Transform for feature extraction and neurode input integration for features classification (section 4). The reason for using neural-net technology is simple enough: It can solve problems that conventional statistical methods cannot, at least not within acceptable cost/performance criteria.
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Fuzzy C-Means (FCM)
Fuzzy sets are a generalization of conventional set theory introduced by Zadeh as a mathematical way to represent vagueness in everyday life. Fuzzy interpretations of data structures are a very natural and plausible way to formulate and solve various problems [Ill. It appears that medical data interpretation may be an specially fruitful area for fuzzy clustering, since biological systems are extremely complex and the boundaries between their classes are not sharply defined. The approach outlined in this paper is based on the premise that the key elements in human thinking are not numbers, but labels of fuzzy sets, that is, classes of objects in which the transition from membership to non-membership is gradual rather than abrupt. The degree of muscle contraction and the concomitant myoelectrical signal generation does not escape to such rule. In this case, it is not appropriate to give an exact numerical representation to uncertain feature data. Rather, it may be convenient to represent uncertain feature information by fuzzy subsets. For this reasons, it is reasonable to use linguistic variables in order to describe the EMG feature information (e.g. resting, slight contraction, moderate contraction, strong contraction). The most well known objective function for clustering in X is the classical within groups sum of squared errors function subsequently modified by Bezdek, defined as
where v = ( V I , v 2 , ...,vc) is a vector of (unknown) cluster centers vi E Rpfwl 5 i 5 c, m E [l,a)is a weighting exponent on each fuzzy membership, A = any positive definitive (p x p) matrix , and U is a hard or conventional c-partition of X. Optimal partitions U ’ o f X are taken from pairs (U’, .U*) that are local minimizers of 51. The basic idea in FCM is to minimize J, over the variables U and v on the assumption that matrices U that are part of optimal pairs for J, identify “good” partitions of the data. Algorithms as FCM and fuzzy ISODATA are among the most popular set of algorithms that perform this task.
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3
Neural Network Interface
Adaptive interfaces are a natural and important clers of applications for artificial neural networb (ANN). Such adaptive interfaces simplify the proceu of designing a compatible mapping and, also, allow the mapping to be tailored to each individual user. Error-back propagation method ir wed aa learning procedure for multilayered, feedforward neural networb. By means of this procedure, the network can lean to map a set of inputs to a set of outputr. The network topology chosen waa the feedforward variety with one input layer containing 64 input neurodes, one hidden layer with two neurodes, and one output neurode. The network is initialised with coupling strength belonging to previous experience to curtail the training time for the new subject’s data. In practice, error-back propagation has proven to be a suitable algorithm in establishing a set of coupling strengths that enable the network to perform certain input-output mappings. The ability to discriminate between different ME signala relied on the clersifier’s capacity to truly identify separable regions in the 64dimensiond feature space.
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Implementation
A prototype based on the Motorola DSP56001 Application Development Module was designed in our laboratory. The DSP56001 is a member of Motorola’s family of HCMOS, 56-bit general purpose Digital Signal Proceaaor. It architecture is optimired to execute DSP algorithms in few operations, while maintaining a high degree accuracy. Electromyographic power spectra waa computed after sampling the MES incoming from four surface electrodes placed over the biceps braquii. Digitination of the signal wan done by using a 16-bit deltccsigma modulation A/D converter. The sampling rate wan set to convert four channels at a frequency of 1,953 Hz each by meam of an analog multiplexer. Anti-aliasing filtering waa provided by band-limiting the signal to 20-500 HZ range by a fourth-order, maximally flat, bend-paaa filter in each of the channels. Two programs were written in Motorola DSP56001 assembly code, maximting the efficiency of the processor pipeline structure and at the same time avoiding high-level language overhead. The first program performed the acquisition of 256-samples of each channel, window-weighted by a Hamming function and transformed into the frequency domain. Thus, a 256-point, DIT, complete FFT on real data waa used, performing 24bit fixed-point arithmetic thereby providing 144 dB of dynamic range. Four MES data buffers from a contracting muscle were transformed into the frequency domain with 7.6 Ht frequency resolution. These data was downloaded to the host computer and saved in magnetic media. The feature space, represented by 64dimension spectral vectors, served aa training data to the neural network interface. Ninty records at various levels of muscle contraction being represented by ten records each, were obtained from different subjects. A total of nine hundred power spectrum patterns and three hundred environmental noise patterns were subjected to be learned by an off-line multilayer-perceptron algorithm running on a DECstation 5000/125 computer. The training data waa recursively clustered within three characteristic clerses by the use of the furry c-means algorithm. This unsupervised process provided a sufficient partition of the feature space in nine claseee, assigning to the original data a corresponding target value. With an appropriate selection of the gain and momentum parameter it wan possible to obtain network convergence within 2500 iterations. Weights obtained were used in a second program executed on the DSP56001 Application Development Module for real time testing. The second program performed the upload of obtained weights and enter the endless loop of MES acquisition, Fourier transformation, r e d phase and output the result through a D/A converter. In terms of implementation, the neurode input integration is easily coded, and executed by a DSP processor with minimum computational cost. At 10.25 MIPS the DSP56001 executes this code at 140.75 ms. per result obtained, at the expense of g3% of the time of data acquisition (131.07 ms.). The prototype achieved real-time operation by delivering 7.6 results per second corresponding to four incoming signal’s discrimination.
5 5.1
Results MES Clustering
The MES feature space waa clustered using fussy c-mean into nine c l a ” of muscle contraction aa a function of force. This was obtained by clustering the available data into three b e s aa mating, moderate contraction, and strong contraction followed by a recursive second level of data clustering. Therefore, each of the original
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classes was clustered into three additional levels of muscle contraction, improving the precision of the target values. The feature space originally constituted by 64 dimensions was, then, represented in a 3-D plot given by the three membership functions obtained on each of the clustering process. Results were validated by visual inspection and correlated with the corresponding force impinged to the muscle showing a significant degree of certainty. Efforts were made to avoid muscle fatigue since spectral shifts would lead to erroneous results.
Discrimination of the MES from noise patterns
5.2
Evaluation of the device which recognize the MES requires test signals that simulate its spectral behavior during muscle contraction. Sine wave analysis, even when suboptimal because it cannot represents the stochastic nature of the MES, was used to assess the device accuracy. Results reaching full recognition of a pure sine wave were achieved within f 7-10 Hz accuracy (approximately the system frequency resolution). To demonstrate the ability of the system to recognize MES incoming patterns, different experiments were carried out. Experiments in which the MES was obtained purely without noise intervention and vice-versa were recognized individually to a full extend, obtaining a total correlation of almost 97% (Figure 1). This is explained by the fact that the network learned to map different patterns but not a combination of them. A good example was the inclusion of a 60 Ha. signal among the interference patterns. The network recognized successfully the presence of 60 Hz. in the body and output a null value accordingly. However, this was achieved only when no MES was sensed, given that its presence would masked the 60 Hz as it falls in the EMG power spectrum. Electromagnetic interference provided a non-periodic noise very much alike to the MES in the time-domain. In the frequency domain, the high energy at higher frequency components determined a difference with the MES and was able to be discriminated by the neural-net with high accuracy (Figure 2).
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Figure 1: ANN real time response for (a) resting muscle and (b) moderate contraction
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Figure 2: ANN real time response for (c) strong contraction and (d) MES masked by noise
Table 1 shows the degree of expected network response to known incoming patterns. Resting and Nowe patterns achieved a highly accurate response. Slight through Strong contractions showed a slight "leakage" on the neighbor classes due to the stochastic nature of the MES given the time varying firing rate of the motor units.
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Table 1: Distribution of real-time muscle contraction recognition
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Discussion
Fuzzy c-means clustering and a neural-network model, back propagation, has been applied to the recognition of the MES in real-time. It has been demonstrated the ability of fuzzy clustering the training data into different degrees and subdegrees of muscle contraction. Back-propagation, even when showed limited performance
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in certain cases of pattern recognition, had shown satisfactory results in terms of feature classification and speed in both, training and recall phase. This type of MES discrimination from noise patterns is a significant result. MES analysis by traditional techniques lead the subject to undergo many hours of training so that he or she is able to generate repeatable MES which can be recognized successfully. Our work represents not only an advance on MES signal recognition in real-time but also, it curtails subject’s training to a minimum. We had, also, addressed the problem having in mind the high intersubject and intrasubject variability of the MES LE. Neural networks architectures provide a twofold solution, a fast way of system customization to the patient and a better patient adaptation to the system, hopefully improving the low rate of acceptance of this devices.
Acknowledgements The research described in this paper was supported in part by National Science Foundation grant CDA9313624. We express our gratitude to Motorola Co. for their support through the DSP University Support Program. We thank ICRL members at FIU for fruitful discussions.
References [l] Saridis G., Gootee T., “EMG pattern analysis and classification for a prosthetic arm,”IEEE Transactions on Biomedical Engineering, Jun 1982, (29):403-412 [2] Hefftner G., “The electromyogram as a control signal for functional neuromuscular stimulation- Part I: Autoregressive modeling as a mean of MES discrimination,’’ IEEE Transactions on Biomedical Engineering, April 1988, 35(4):230-237. [3] Shiavi R. “Electromyographic pattern in adult locomotion: A comprehensive review,”J . Rehab. Res. Develop., Jun 1985, 22(3):85-89 [4] Chen J., “A quantitative and qualitative description of electromyographic linear envelopes for synergy analysis,” IEEE Transactions on Biomedical Engineering, Jan. 1992, 39( 1):9-18 [5] Philipson, L., “The electromyographic signal as a measure of muscular force: a comparison of detection and quantification techniques,” Electromyogr. Clin. Neurophysiol., 1988,(28):141-150. [6] Fuglasang, A., “Quantitative electromyography. I. Modifications of the turns analysis,”Electromyogr. Clin. Neurophysiol., 1987,(27):335-338. [7] Inbar G., Noujaim E. ,“On surface EMG spectral characterization and its application to diagnostic classification,”IEEE Transactions on Biomedical Engineering, 1984,(31):597-604 [8] Zhang Li-Qun, “Clustering Analysis and Pattern Discrimination of EMG Linear Envelopes,”IEEE Transrrctions on Biomedical Engineering, Aug 1991, 38(8):777-784 [9] Jia-Jin Jason Chen et al., “Temporal Feature Extraction and Clustering Analysis of Electromyographic Linear Envelopes in Gait Studies,” IEEE Transactions on Biomedical Engineering, Mar 1990, 37(3):295302.
[lo] M. Kelly and P. Parker, “The Application of Neural Networks to Myoelectric Signal Analysis: A preliminary Study,”IEEE fiansactions on Biomedical Engineering, Mar 1990, 37(3):221-229. [ll] Bezdek J.C. , “Fuzzy Models- What are they, and Why?,”IEEE Transactions on f i z z y Systems, Feb 1993, l(1):l-5.
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