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. >

A new strategy for multifunction myoelectric control

This paper represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal (MES). The scheme described within uses pattern recognition to process four channels of MES, with the task of discriminating multiple classes of limb movement. The method does not require segmentation of the MES data, allowing a continuous stream of class decisions to be delivered to a prosthetic device. It is shown in this paper that, by exploiting the processing power inherent in current computing systems, substantial gains in classifier accuracy and response time are possible. Other important characteristics for prosthetic control systems are met as well. Due to the fact that the classifier learns the muscle activation patterns for each desired class for each individual, a natural control actuation results. The continuous decision stream allows complex sequences of manipulation involving multiple joints to be performed without interruption. Finally, minimal storage capacity is required, which is an important factor in embedded control systems.

A robust, real-time control scheme for multifunction myoelectric control

Feature extraction is a significant method to extract the useful information which is hidden in surface electromyography (EMG) signal and to remove the unwanted part and interferences. To be successful in classification of the EMG signal, selection of a feature vector ought to be carefully considered. However, numerous studies of the EMG signal classification have used a feature set that have contained a number of redundant features. In this study, most complete and up-to-date thirty-seven time domain and frequency domain features have been proposed to be studied their properties. The results, which were verified by scatter plot of features, statistical analysis and classifier, indicated that most time domain features are superfluity and redundancy. They can be grouped according to mathematical property and information into four main types: energy and complexity, frequency, prediction model, and time-dependence. On the other hand, all frequency domain features are calculated based on statistical parameters of EMG power spectral density. Its performance in class separability viewpoint is not suitable for EMG recognition system. Recommendation of features to avoid the usage of redundant features for classifier in EMG signal classification applications is also proposed in this study.

Feature reduction and selection for EMG signal classification

Myoelectric control systems—A survey

This paper examines the postulate that an important function of the activity of antagonist muscle groups is to modulate mechanical impedance. Some biomechanical modeling and analyses are presented leading to a prediction of simultaneous activation of antagonist muscles in the maintenance of upright posture of the forearm and hand. An experimental observation of antagonist coactivation in this situation is presented.

Adaptive control of mechanical impedance by coactivation of antagonist muscles

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. >

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

The development of myoelectric control systems for powered limb prostheses has advanced rapidly in recent years. The main thrusts in this development have been in realizing self-contained prostheses and in realizing better prostheses control through improvements in the myoelectric signal processing techniques. This review considers the latter of these two areas. It first presents an historical look at myoelectric signal processing and identifies the problems. It then presents a general look at the myoelectric signal and those characteristics which give rise to these problems. A review of the literature related to various control strategies and signal processing techniques to overcome these problems is given. Finally, future trends to be expected in this area are discussed.

Myoelectric control of prostheses.

The following is a brief introduction to powered prosthetics and myoelectric control. This paper reviews the present availability and clinical impact of myoelectric prostheses. A significant observation is that these systems have reached a sufficient degree of maturity that they are accepted by many health-care funding agencies as reasonable and cost-effective components of the rehabilitation process. The limitations of both the mechanical systems and the myoelectric controls are discussed in some detail, from the viewpoint of the potential user. Finally, an overview is given of current research in this field with comments on probable directions of development.

Myoelectric Prostheses: state of the art

A theoretical model of the electromyographic (EMG) signal has been developed In the model, the neural pulse train inputs were considered to be point processes which passed through linear, time-invariant systems that represented the respective motor unit action potential The outputs were then summed to produce the EMG It was assumed, that in the production of muscle force, the controlled parameter was the number of active motor units, n(t) The model then showed that the EMG can be represented as an amplitude modulation process of the form EMG = [Kn(t)1/2 w(t) with the stochastic process, w(t), having the spectral and probability characteristics of the EMG during a constant contraction Various assumptions made in the model development have been verified by experiments

Robert N. Scott

Papers

A new strategy for multifunction myoelectric control

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

Myoelectric control of prostheses.

Myoelectric Prostheses: state of the art

A Nonstationary Model for the Electromyogram