Philip A. Parker

A new strategy for multifunction myoelectric control

A novel approach to the control of a multifunction prosthesis based on the classification of myoelectric patterns is described. It is shown that the myoelectric signal exhibits a deterministic structure during the initial phase of a muscle contraction. Features are extracted from several time segments of the myoelectric signal to preserve pattern structure. These features are then classified using an artificial neural network. The control signals are derived from natural contraction patterns which can be produced reliably with little subject training. The new control scheme increases the number of functions which can be controlled by a single channel of myoelectric signal but does so in a way which does not increase the effort required by the amputee. Results are presented to support this approach.<<ETX>>

Classification of the myoelectric signal using time-frequency based representations

An accurate and computationally efficient means of classifying surface myoelectric signal patterns has been the subject of considerable research effort in recent years. Effective feature extraction is crucial to reliable classification and, in the quest to improve the accuracy of transient myoelectric signal pattern classification, an ensemble of time-frequency based representations are proposed. It is shown that feature sets based upon the short-time Fourier transform, the wavelet transform, and the wavelet packet transform provide an effective representation for classification, provided that they are subject to an appropriate form of dimensionality reduction.

Fuzzy EMG classification for prosthesis control

This paper proposes a fuzzy approach to classify single-site electromyograph (EMG) signals for multifunctional prosthesis control. While the classification problem is the focus of this paper, the ultimate goal is to improve myoelectric system control performance, and classification is an essential step in the control. Time segmented features are fed to a fuzzy system for training and classification. In order to obtain acceptable training speed and realistic fuzzy system structure, these features are clustered without supervision using the Basic Isodata algorithm at the beginning of the training phase, and the clustering results are used in initializing the fuzzy system parameters. Afterwards, fuzzy rules in the system are trained with the back-propagation algorithm. The fuzzy approach was compared with an artificial neural network (ANN) method on four subjects, and very similar classification results were obtained. It is superior to the latter in at least three points: slightly higher recognition rate; insensitivity to overtraining; and consistent outputs demonstrating higher reliability. Some potential advantages of the fuzzy approach over the ANN approach are also discussed.

Extracting Simultaneous and Proportional Neural Control Information for Multiple-DOF Prostheses From the Surface Electromyographic Signal

A novel signal processing algorithm for the surface electromyogram (EMG) is proposed to extract simultaneous and proportional control information for multiple DOFs. The algorithm is based on a generative model for the surface EMG. The model assumes that synergistic muscles share spinal neural drives, which correspond to the intended activations of different DOFs of natural movements and are embedded within the surface EMG. A DOF-wise nonnegative matrix factorization (NMF) is developed to estimate neural control information from the multichannel surface EMG. It is shown, both by simulation and experimental studies, that the proposed algorithm is able to extract the multidimensional control information simultaneously. A direct application of the proposed method would be providing simultaneous and proportional control of multifunction myoelectric prostheses.

The application of neural networks to myoelectric signal analysis: a preliminary study

Two neural network implementations are applied to myoelectric signal (MES) analysis tasks. The motivation behind this research is to explore more reliable methods of deriving control for multi-degree-of-freedom arm prostheses. A discrete Hopfield network is used to calculate the time series parameter for a moving average MES model. It is demonstrated that the Hopfield network is capable of generating the same time series parameters as those produced by the conventional sequential least-squares algorithm. Furthermore, it can be extended to applications utilizing larger amounts of data, and possibly to higher-order time series models, without significant degradation in computational efficiency. The second neural network implementation involves using a two-layer perceptron for classifying a single-site MES on the basis of two features, the first time series parameter and the signal power.<<ETX>>

Control of Upper Limb Prostheses: Terminology and Proportional Myoelectric Control—A Review

The recent introduction of novel multifunction hands as well as new control paradigms increase the demand for advanced prosthetic control systems. In this context, an unambiguous terminology and a good understanding of the nature of the control problem is important for efficient research and communication concerning the subject. Thus, one purpose of this paper is to suggest an unambiguous taxonomy, applicable to control systems for upper limb prostheses and also to prostheses in general. A functionally partitioned model of the prosthesis control problem is also presented along with the taxonomy. In the second half of the paper, the suggested taxonomy has been exploited in a comprehensive literature review on proportional myoelectric control of upper limb prostheses. The review revealed that the methods for system training have not matured at the same pace as the novel multifunction prostheses and more advanced intent interpretation methods. Few publications exist regarding the choice of training method and the composition of the training data set. In this context, the notion of outcome measures is essential. By definition, system training involves optimization, and the quality of the results depends heavily on the choice of appropriate optimization criteria. In order to further promote the development of proportional myoelectric control, these topics need to be addressed.

Signal processing for the multistate myoelectric channel

In the multistate myoelectric channel, a single myoelectric signal source is used to control a multifunction powered prosthesis. The selection of a prosthesis function requires a receiver to process the myoelectric signal, contaminated with noise, and to decide on the basis of the received signals which function is desired. Thus the channnel cleady presents a problem of choice of receiver and of decision strategy. Previous sotutions to this problem have been basically empirical. In this paper we seek the optimum receiver where optimum is in the minimum probability of error sense. First a model is developed for the bipolar myoelectric signal to provide information about the relevant signal parameters and statistics. Using this information the Bayes minimum probability of error receiver is derived for an orbitrary signal parameter set. The optimum signal parameter set is then found for the Bayes receiver, and the receiver performance calculated. The receiver performance is measured and compared with the calculated performance. A significant performance improvement is seen in the optimum receiver over a more conventional receiver.

The short-time Fourier transform and muscle fatigue assessment in dynamic contractions.

The mean frequency of the power spectrum of an electromyographic signal is an accepted index for monitoring fatigue in static contractions. There is however, indication that it may be a useful index even in dynamic contractions in which muscle length and/or force may vary. The objective of this investigation was to explore this possibility. An examination of the effects of amplitude modulation on modeled electromyographic signals revealed that changes in variance created in this way do not sufficiently affect characteristic frequency data to obscure a trend with fatigue. This validated the contention that not all non-stationarities in signals necessarily manifest in power spectral parameters. While an investigation of the nature and effects of non-stationarities in real electromyographic signals produced from dynamic contractions indicated that a more complex model is warranted, the results also indicated that averaging associated with estimating spectral parameters with the short-time Fourier transform can control the effects of the more complex non-stationarities. Finally, a fatigue test involving dynamic contractions at a force level under 30% of peak voluntary dynamic range, validated that it was possible to track fatigue in dynamic contractions using a traditional short-time Fourier transform methodology.

Motor unit power spectrum and firing rate

Changes in the power density spectrum of myoelectric signal with contraction level have been reported in the literature. These changes can be induced by a number of possible factors including recruitment of differing types of units, conduction velocity changes and firing rate changes. In the paper the single unit power spectrum is investigated and the effects of firing rate mean and variance changes evaluated. Motor unit signal simulation and experiments are carried out to verify predictions. The results show that spectrum peaks will shift with firing rate and the median frequency is weakly dependent on firing rate.

Noise characteristics of stainless-steel surface electrodes

Bioelectric events measured with surface electrodes are subject to noise components which may be significant in comparison with low-level biological signals such as evoked neuroelectric potentials, and myoelectric potentials. In an effort to better understand noise arising from these electrodes, electrode and measurement system noise is modelled. The effect of electrode surface area on electrode impedance and noise is studied using circular stainless-steel electrodes of varying diameters. The main contributions of the work are the development of a model for stainless-steel electrode noise as a function of electrode area, and demonstrating that, for the band-width of interest to evoked neuroelectric and myoelectric signals (8–10 000 Hz), the primary noise components are thermal and amplifier current generated. The magnitudes of both of these depend on the electrode impedance magnitude. Electrode impedance is shown to be a power function of both electrode diameter and frequency, consistent with a capacitive electrode model.