Daniel Graupe

Functional Separation of EMG Signals via ARMA Identification Methods for Prosthesis Control Purposes

Multifunctional control of artificial limbs via electromyographic (EMG) actuation requires means for reliably recognizing or distinguishing between the various functions on the basis of the recorded EMG data. Furthermore, constraints of weight, cost, and computation time on practical prosthesis application must be satisfied. An approach to the aforementioned recognition problem is given in terms of deriving a fast parametric-recognition algorithm whereby the autoregressive-moving-average (ARMA) parameters and the Kalman filter parameters of the EMG time series are identified. It is shown that the resulting identified parameters yield sufficient information to discriminate between a small number of upper extremity functions. Problems involved in practical prosthesis control via the present approach and problems of hardware realization are discussed to illustrate the validity of the approach.

Multifunctional prosthesis and orthosis control via microcomputer identification of temporal pattern differences in single-site myoelectric signals

The paper discusses results of on-line tests on amputees and hemiplegics of multifunctional prostheses and orthoses control by identifying the parameters of single-site temporal EMG signal signatures. The results relate to tests on above-elbow amputees, on shoulder-disarticulation amputees (including a congenital disarticulation amputee) and on hemiplegics, varying from 5 to 50 years of age. The system employed is based on an 8-bit Intel 8080 microprocessor, when computation is in double precision, to obtain an effective 16-bit work-length. The system employs a sequential least-squares algorithm to identify a 4-parameter auto-regressive time-series model of the EMG signal, and a Bayesian rule discrimination algorithm.

EMG pattern analysis for patient-responsive control of FES in paraplegics for walker-supported walking

The use of electromyographic (EMG) pattern analysis to provide upper-motor-neuron paraplegics with patient-responsive control of FES (functional electrical stimulation) for the purpose of walker-supported walking is discussed. The system described uses above-lesion surface EMG signals to activate standing and walking functions in a patient-responsive manner. This system was been experimentally applied to paraplegics since early 1982. Below-lesion response-EMG control from the stimulated sites was added in 1987 to regulate stimuli levels in the face of fatigue. Although transcutaneous FES alone is being used the system is applicable in principle to implantable FES systems.<<ETX>>

Stochastic analysis of myoelectric temporal signatures for multifunctional single-site activation of prostheses and orthoses

This paper is concerned with a stochastic time-series analysis of the temporal signatures of myoelectric (ME) signals including the determination of model order and sampling rate. The paper considers the use of time-series parameters for the activation of artificial limbs for high-level amputees, of stimulation electrodes or of powered braces for paralysed persons, in several degrees of freedom, from a single or two surface-electrode pairs at locations where considerable ME cross-talk exists. The multifunctional capability from a single site is based on the differences between the time-series (TS) parameters for different muscle activation patterns at the same ME site, these differences being thus used for limb function discrimination via easily trainable muscle activation patterns at the vicinity of the electrode site. Specifically, the analysis is in terms of identifying the AR parameters of a time-domain autoregressive (AR) signature model both for the complete ME spectrum and for parts thereof, and in terms of the autocorrelation of the signal and of the models residual. Determination of sampling rate and of model orders is discussed in detail. It is shown that, using online real-time analysis, differences in the AR time-series parameters can be observed for different trainable patterns of muscle activation, at the same electrode location, even at the same ME power levels, as long as considerable cross-talk exists at the electrode site. These parameter differences can be accentuated if one considers the AR parameters for lower-frequency spectral windows. A case is made in this paper for employing TS analysis to squeeze out information in a distinct but low-level ripple of the low frequency spectrum of the signal. This information tends to be ignored in frequency domain, but is all that the AR parameters care for in TS analysis, since they are not concerned, with a flat-average low-frequency spectrum, i.e., its white-noise-like part, which is the residual term of the AR Model and not an AR parameter. Discrimination between different functions from a single electrode-site, at even the same power level, is thus shown to require considerable cross-talk at the given site, and to require the consideration of only the low-frequency part of the spectrum.

A novel large-memory neural network as an aid in medical diagnosis applications

Describes the application of a LAMSTAR (LArge Memory STorage And Retrieval) neural network to medical diagnosis and medical information retrieval problems. The network is based on M.L. Minskys (1980) knowledge lines (k-lines) theory of memory storage and retrieval in the central nervous system. It employs arrays of self-organized map modules, such that the k-lines are implemented via link weights (address correlation) that are updated by learning. The network also employs features of forgetting and of interpolation and extrapolation, and is thus able to handle incomplete data sets. It can deal equally well with exact and fuzzy information, thus making it specifically applicable to medical diagnosis where the diagnosis is based on exact data, fuzzy patient interview information, patient histories, observed images and test records. Furthermore, the network can be operated in a closed loop with search engines to intelligently use data from the Internet in a higher learning hierarchy. All of the above features are shown to make the LAMSTAR network suitable for medical diagnosis problems that concern large data sets of many categories that are often incomplete and fuzzy. Applications of the network to three specific medical diagnosis problems are described: two from nephrology and one related to an emergency-room drug identification problem. It is shown that the LAMSTAR network is hundreds, and even thousands, times faster in its training than backpropagation-based networks when used for the same problem with exactly the same information.

Functional neuromuscular stimulator for short-distance ambulation by certain thoracic-level spinal-cord-injured paraplegics

BACKGROUND Functional Neuromuscular Stimulation (FNS) for unbraced short-distance ambulation by traumatic complete/near-complete T4 to T12 paraplegics is based on work by Graupe et al (1982), Kralj et al (1980), Liberson et al (1961), and others. This paper discusses methodology, performance, training, admissibility criteria, and medical observations for FNS-ambulation using the Parastep-I system, which is the first and only such system to have received FDA approval (1994) and which emanated from these previous works. METHOD The Parastep system is a transcutaneous non-invasive and microcomputerized electrical stimulation system built into a Walkman-size unit powered by eight AA batteries that is controlled by finger-touch buttons located on a walkers handbars for manual selection of stimulation menus. The microcomputer shapes, controls, and distributes trains of stimulation signals that trigger action potentials in selected peripheral nerves. Walker support is used for balance. The patient can don the system in under 10 minutes. At least 32 training sessions are required. RESULTS Approximately 400 patients have used the Parastep system, essentially all achieving standing and at least 30 feet of ambulation, with a few reaching as much as 1 mile at a time. Recent literature presents data on the medical benefits of using the Parastep system-beyond the exercise benefits of short distance ambulation at will-such as increased blood flow to the lower extremities, lower HR at subpeak work intensities, increased peak work capability, reduced spasticity, and psychological benefits. CONCLUSIONS We believe that the Parastep FNS system, which is presently commercially available by prescription, is easily usable for independent short-distance ambulation. We believe that its exercise benefits and its other medical and psychological benefits, as discussed, make it an important option for thoracic-level traumatic paraplegics.

Identification of autoregressive moving-average parameters of time series

A procedure for sequentially estimating the parameters and orders of mixed autoregressive moving-average signal models from time-series data is presented. Identification is performed by first identifying a purely autoregressive signal model. The parameters and orders of the mixed autoregressive moving-average process are then given from the solution of simple algebraic equations involving the purely autoregressive model parameters.

Method of and means for adaptively filtering screeching noise caused by acoustic feedback

A communication system, such as a hearing aid or a public address system, may include a microphone for inputting audio information to the system, an amplifier for amplifying audio frequency signals inputted to the microphone, and a speaker for outputting amplified audio frequency signals into the environment which provides an acoustic feedback path between the speaker and the microphone. The invention provides an identification circuit for dynamically identifying those parameters associated only with acoustic feedback, and a correction circuit whose transfer function is established in accordance with the parameters identified by the identification circuit. The transfer function of the correction circuit is such that the effect of acoustic feedback is cancelled from the transfer function of communication system. The identification circuit is constructed and arranged to identify said parameters in response to a turn-on of the system, or to an automatically-sensed threshold change in gain of the amplifier.

Topographic component (Parallel Factor) analysis of multichannel evoked potentials: Practical issues in trilinear spatiotemporal decomposition

SummaryWe describe a substantive application of the trilinear topographic components /parallel factors model (TC/PARAFAC, due to Möcks/Harshman) to the decomposition of multichannel evoked potentials (MEPs). We provide practical guidelines and procedures for applying PARAFAC methodology to MEP decomposition. Specifically, we apply techniques of data preprocessing, orthogonality constraints, and validation of solutions in a complete TC analysis, for the first time using actual MEP data. The TC model is shown to be superior to the traditional bilinear principal components model in terms of data reduction, confirming the advantage of the TC models added assumptions. The model is then shown to provide a unique spatiotemporal decomposition that is reproducible in different subject groups. The components are shown to be consistent with spatial/temporal features evident in the data, except for an artificial component resulting from latency jitter. Subject scores on this component are shown to reflect peak latencies in the data, suggesting a new aspect to statistical analyses based on subject scores. In general, the results support the conclusion that the TC model is a promising alternative to principal components for data reduction and analysis of MEPs.

Patient controlled electrical stimulation via EMG signature discrimination for providing certain paraplegics with primitive walking functions.

Abstract In this paper we present the first patient-based results for a micro computer-based EMG signature-controlled functional electrical stimulation (FES) system, for restoring walker-supported and brace-free primitive walking to complete paraplegics, at the patients own command. Stimulation is thus controlled directly by the patients own EMG signatures, generated by him at will, and which are produced by him at his erector spinae back muscles (while activating these muscles more or less as he would during normal walking, had the person not been a paraplegic). In this manner a switch-free simplistic but reliable information-gap is produced across the paraplegics lesion, such that the above lesions relatively normal EMG provides the control command to electrical stimulation of paralyzed peripheral (lower limb) nerves, to provide the basic functions of standing up, sitting-down and walking. The FES signals are at pulse rates close to the average ones occurring naturally at the corresponding nerves. The paper reports actual patient results for 3 paraplegics, two at T-9 (one complete, one with only residual sensation) and one T-6 complete paraplegic, who all subsequently achieved walking between parallel bars and two even with a walker (no braces in all cases), using the FES system, this walking being, to our knowledge the first hand-switch-free patient-controlled FES walking reported ever. The EMG signature-discrimination for control is as previously developed by D. Graupe for artificial-limb control, and it depends only on the EMG signature temporal parameters, while completely ignoring EMG power (amplitude) level. Whereas all 3 patients produced EMG parameters in the range reported below, adequate for controlling all functions involved, only one, a T-6 complete paraplegic, has so far (due to time limitations) achieved EMG controlled walking between parallel bars and by now also a few steps with a walker. One T-9 complete paraplegic, injured 5 years before coming to our program, was however able, within 4 weeks, to walk via FES with a walker, though with manual control. Note that the present approach is valid only for upper motor neuron paralysis situations (intact peripheral nerves and muscles). We are thus also attempting to apply this approach to certain hemiplegics and quadraplegics, for restoring some upper limb functions, using EMG signals from the trapezius (shoulder) level.