B. Hudgins

A Real-Time Pattern Recognition Based Myoelectric Control Usability Study Implemented in a Virtual Environment

Pattern recognition based myoelectric control systems have been well researched; however very few systems have been implemented in a clinical environment. Although classification accuracy or classification error is the metric most often reported to describe how well these control systems perform, very little work research has been conducted to relate this measure to the usability of the system. This work presents a virtual clothespin usability test to assess the performance of pattern recognition based myoelectric control systems. The results suggest that users can complete the virtual task in reasonable time frames when using systems with high classification accuracies. Additionally, results indicate that a clinically-supported classifier training approach (inclusion of the transient potion of contraction signals) may reduce classification accuracy but increase real-time performance.

The effect of electrode displacements on pattern recognition based myoelectric control.

Pattern recognition based myoelectric controllers rely on a fundamental assumption that the patterns detected under a given electrode are repeatable for a given state of muscle activation. Consequently, electrode displacements on the skins surface affect the classification accuracy of the pattern based myoelectric controller. The effects of electrode displacement can be mitigated by using a training set of data which consists of patterns detected over a range of plausible displacement locations to train the control system

Hidden Markov model classification of myoelectric signals in speech

It has been demonstrated that myoelectric signal (MES) automatic speech recognition (ASR) using an hidden Markov model (HMM) classifier is resilient to temporal variance, which offers improved robustness compared to the linear discriminant analysis (LDA) classifier. The overall performance of the MES ASR can be further enhanced by optimizing the features and structure of the HMM classifier to improve classification rate. Nevertheless, the HMM classifier has already shown that it would effectively complement an acoustic classifier in a multimodal ASR system.

Myo-electric signals to augment speech recognition

It is proposed that myo-electric signals can be used to augment conventional speech-recognition systems to improve their performance under acoustically noisy conditions (e.g. in an aircraft cockpit). A preliminary study is performed to ascertain the presence of speech information within myo-electric signals from facial muscles. Five surface myo-electric signals are recorded during speech, using Ag−AgCl button electrodes embedded in a pilot oxygen mask. An acoustic channel is also recorded to enable segmentation of the recorded myo-electric signal. These segments are processed off-line, using a wavelet transform feature set, and classified with linear discriminant analysis. Two experiments are performed, using a ten-word vocabulary consisting of the numbers ‘zero’ to ‘nine’. Five subjects are tested in the first experiment, where the vocabulary is not randomised. Subjects repeat each word continuously for 1 min; classification errors range from 0.0% to 6.1%. Two of the subjects perform the second experiment, saying words from the vocabulary randomly; classification errors are 2.7% and 10.4%. The results demonstrate that there is excellent potential for using surface myo-electric signals to enhance the performance of a conventional speech-recognition system.

Improving myoelectric signal classification using wavelet packets and principal components analysis

An accurate and computationally efficient means of classifying surface myoelectric signals has been the subject of considerable research effort in recent years. This work demonstrates how this may be achieved, using a wavelet packet based feature set in conjunction with principal components analysis.

A dynamic feedforward neural network for subset classification of myoelectric signal patterns

Many biological signals are transient in nature, and the myoelectric signal (MES) is no exception. This is problematic for pattern classifiers that fail to incorporate the structure present in the temporal dimension of these signals. Standard feedforward neural network classifiers have difficulty processing temporal signals-time cannot be implicitly represented by the network architecture. A dynamic feedforward neural network architecture is described here that more effectively integrates the temporal information in transient signals, The internal representation of time also allows the dynamic network to classify subsets of the full temporal record. This reduces the time needed to obtain a classification result-an obvious benefit to real-time identification applications, such as the control of prosthetic devices.

A novel approach to surface electromyography: an exploratory study of electrode-pair selection based on signal characteristics

A 3u2009×u20094 electrode array was placed over each of seven muscles and surface electromyography (sEMG) data were collected during isometric contractions. For each array, nine bipolar electrode pairs were formed off-line and sEMG parameters were calculated and evaluated based on repeatability across trials and comparison to an anatomically placed electrode pair. The use of time-domain parameters for the selection of an electrode pair from within a grid-like array may improve upon existing electrode placement methodologies.

Myoelectric signal as a quantitative measure of muscle mechanical output

The estimation of muscle tension and velocity of shortening from the myoelectric signal have been considered in numerous papers. These papers consider the estimates of each variable separately, with the other appearing in the estimation as a constant parameter. The work described in this paper develops a model for the relationship between a muscle’s mechanical outputs and the myoelectric signal. The model suggests that the myoelectric signal is related directly to the muscle mechanical power via a nonlinear differential equation in velocity of shortening. The model is general in that it includes as special cases the isometric and anisometric constant-velocity work of other authors and agrees with their experimental results. In this work anisometric experiments are performed on the biceps brachii muscle to verify the model. Estimates of muscle velocity of shortening and mechanical power are obtained from the myoelectric signal during anisometric contractions and the results agree well with the actual velocity and power. The model points out that the myoelectric signal is a direct measure of muscle tension only under isometric conditions.

Optimized Gaussian mixture models for upper limb motion classification

This work introduces the use of Gaussian mixture models (GMM) for discriminating multiple classes of limb motions using continuous myoelectric signals (MES). The purpose of this work is to investigate an optimum configuration of a GMM-based limb motion classification scheme. For this effort, a complete experimental evaluation of the Gaussian mixture motion model is conducted on a 12-subject database. The experiments examine algorithmic issues of the GMM including the model order selection and variance limiting. The final classification performance of this GMM system has been compared with that of three other classifiers (a linear discriminant analysis (LDA), a linear perceptron neural network (LP) and a multilayer perceptron (MLP) neural network) . The Gaussian mixture motion model attains 96.3% classification accuracy using four channel MES for distinguishing six limb motions and is shown to outperform the other motion modeling techniques on an identical six limb motion task.

A multi-expert speech recognition system using acoustic and myoelectric signals

Performance of conventional automatic speech recognition systems, which uses only the acoustic signal, is severely degraded by acoustic noise. The myoelectric signal from articulatory muscles of the face is proposed as a secondary source of speech information to enhance conventional automatic speech recognition systems. An acoustic speech expert and myoelectric speech expert are combined using a novel approach based on evidence theory. Data were collected from 5 subjects across an 18 dB range of noise levels. The classification rate of the acoustic expert decreased dramatically with noise, while the myoelectric signal expert remained relatively unaffected by the noise. This method of combining experts is able to dynamically track the reliability of experts. Classification rates of the multi-expert system were better or near either individual expert at all noise levels.