Bernard Hudgins

• Professor at University of New Brunswick

Muscle synergies have been proposed as a way for the central nervous system (CNS) to simplify the generation of motor commands and they have been shown to explain a large portion of the variation in the muscle patterns across a variety of conditions. However, whether human subjects are able to control prostheses proportionally with a small set of s...

Force estimation based on electromyography (EMG) has been proven to be useful for deriving proportional control for myoelectric devices. Muscle synergies seem to be relevant for force estimation since they are patterns of co-activation of muscles during actions. This study investigates the use of muscle synergies extracted from targeted surface EMG...

This study describes a novel myoelectric control scheme that is capable of motion rejection. As an extension of the commonly used linear discriminant analysis (LDA), this system generates a confidence score for each decision, providing the ability to reject those with a score below a selected threshold. The thresholds are class-specific and affect...

A 3 × 4 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 electro...

In 2007, the University of New Brunswick’s (UNB) Institute of Biomedical Engineering (IBME) received funding from the Canadian government’s Atlantic Innovation Fund program to develop a commercially viable and technologically advanced prosthetic hand system. The 5-year project includes several collaborators namely, the Rehabilitation Institute of C...

This article describes the design and evaluation of two comprehensive strategies for endpoint-based control of multiarticulated powered upper-limb prostheses. One method uses residual shoulder motion position; the other solely uses myoelectric signal pattern classification. Both approaches are calibrated for individual users through a short trainin...

Recent literature in pattern recognition-based myoelectric control has highlighted a disparity between classification accuracy and the usability of upper limb prostheses. This paper suggests that the conventionally defined classification accuracy may be idealistic and may not reflect true clinical performance. Herein, a novel myoelectric control sy...

The development of pattern recognition based myoelectric control systems has historically been governed by a desire for high classification accuracies. However, this accuracy is only defined for exemplars from known classes while active prosthetic use invariably produces extraneous contractions. In this work, a leave-one-out comparison method is in...

This paper describes a novel pattern recognition based myoelectric control system that uses parallel binary classification and class specific thresholds. The system was designed with an intuitive configuration interface, similar to existing conventional myoelectric control systems. The system was assessed quantitatively with a classification error...

This paper introduces an enhanced phoneme-based myoelectric signal (MES) speech recognition system. The system can recognize new words without retraining the phoneme classifier, which is considered to be the main advantage of phoneme-based speech recognition. It is shown that previous systems experience severe performance degradation when new words...

Information extracted from multiple channels of the surface myoelectric signal (MES) recording sites can be used as inputs to control systems for powered upper limb prostheses. For small, closely spaced muscles, such as the muscles in the forearm, the detected MES often contains contributions from more than one muscle, the contribution from each sp...

Pattern recognition based myoelectric control systems rely on detecting repeatable patterns at given electrode locations. This work describes an experiment to determine the effect of electrode displacements on pattern classification accuracy, and a classifier training strategy to accommodate this degradation. The results show that electrode displac...

The surface myoelectric signal (MES) has been used as an input to controllers for powered prostheses for many years. As a result of recent technological advances it is reasonable to assume that there will soon be implantable myoelectric sensors which will enable the internal MES to be used as input to these controllers. An internal MES measurement...

Traditional acoustic speech recognition accuracies have been shown to deteriorate in highly noisy environments. A secondary information source is exploited using surface myoelectric signals (MES) collected from facial articulatory muscles during speech. Words are classified at the phoneme level using a hidden Markov model (HMM) classifier. Acoustic...

Information extracted from signals recorded from multi-channel surface myoelectric signal (MES) recording sites can be used as inputs to control systems for powered prostheses. For small, closely spaced muscles, such as the muscles in the forearm, the detected MES often contains contributions from more than one muscle; the contribution from each sp...

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 rela...

The integration of multiple input sources within a control strategy for powered upper limb prostheses could provide smoother, more intuitive multi-joint reaching movements based on the user's intended motion. The work presented in this paper presents the results of using myoelectric signals (MES) of the shoulder area in combination with the positio...

Progress in myoelectric control technology has over the years been incremental, due in part to the alternating focus of the R&D between control methodology and device hardware. The technology has over the past 50 years or so moved from single muscle control of a single prosthesis function to muscle group activity control of multifunction prostheses...

Classification accuracy of conventional automatic speech recognition (ASR) systems can decrease dramatically under acoustically noisy conditions. To improve classification accuracy and increase system robustness a multiexpert ASR system is implemented. In this system, acoustic speech information is supplemented with information from facial myoelect...

The surface myoelectric signal has found many important applications in research and clinical application. Its use as a system input for the control of powered upper-limb prostheses was first proposed almost 60 years ago. This control approach, referred to as myoelectric control, has been a clinically significant option for limb-deficient individua...

Much research has been done towards developing control systems for artificial hands, elbows, and wrists based on the myoelectric signal (MES). While great effort has gone into developing pattern recognition based control systems for these devices, very little attention has been devoted to the shoulder. This is in part because the majority of ampute...

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 electro...

This paper introduces and evaluates the use of Gaussian mixture models (GMMs) for multiple limb motion classification using continuous myoelectric signals. The focus of this work is to optimize the configuration of this classification scheme. To that end, a complete experimental evaluation of this system is conducted on a 12 subject database. The e...

Introduction Myoelectric Signal as a Control Input Conventional Myoelectric Control Emerging MEC Strategies Summary References

The surface myoelectric signal (MES) has proven to be an effective control input for powered prostheses. Pattern recognition based controllers use multi-channel surface MES as inputs to discriminate between the desired classes of limb activation. There are two major methods which may be pursued to increase the accuracy of the controller: 1) use sig...

Many clinically available, upper-extremity prosthetic limbs provide myoelectric control of a single device, such as a hand, elbow, or wrist. Most commonly, these systems yield control information from myoelectric signal (MES) amplitude [1] or rate of change of MES [2]. Such systems have been beneficial; however, prosthetic users would no doubt find...

A technological solution was investigated as a way of accessing sign language interpretation services from a remote location by people who are deaf. A number of participants including people who are deaf, health professionals, counselors, employers, and sign language interpreters were involved in communication simulations that mimic what occurs in...

This paper 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 mixtu...

The use of technology to access sign language interpreters from a remote location can have a significant impact on the timely access of such services for people who are deaf. The potential integration of such services is contingent on factors such as the availability of suitable equipment and the acceptance of the technological solution by people w...

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...

Rehabilitation services to four remote sites in New Brunswick were delivered via PC-based videoconferencing equipment, using ADSL connections to the Internet. Approximately 40 people used the equipment over 18 months. There were 32 videoconference sessions. A total of 60 questionnaires were returned (a 94% response rate). In 31 of the 32 videoconfe...

This work represents an ongoing investigation of dexterous and natural control of upper extremity prostheses using the myoelectric signal. The scheme described within uses pattern recognition to process four channels of myoelectric signal, with the task of discriminating six classes of limb movement. The method does not require segmentation of the...

Intuitive myoelectric prosthesis control is difficult to achieve due to the absence of proprioceptive feedback, which forces the user to monitor grip pressure by visual information. Existing myoelectric hand prostheses form a single degree of freedom pincer motion that inhibits the stable prehension of a range of objects. Multi-axis hands may addre...

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 an...

It has long been recognized that important features of biomedical signals exist in both the time and frequency domains. More recently, a greater understanding of physiological systems has been achieved by articulating the relationship between the time and frequency characteristics of biological signals. The most widely used tool for analyzing signa...

Noninvasive measurements of somatosensory evoked potentials have both clinical and research applications. The electrical artifact which results from the stimulus is an interference which can distort the evoked signal, and introduce errors in response onset timing estimation.

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-ele...

Myoelectric control of a multifunction artificial arm has been an elusive goal for researchers over the past 30 years. This chapter provides an historical perspective of multifunction myoelectric control and introduces several current research efforts that are addressing this difficult problem.

This work represents an ongoing investigation of dexterous and natural control of powered upper limbs using the myoelectric signal. When approached as a pattern recognition problem, the success of a myoelectric control scheme depends largely on the classification accuracy. A novel approach is described that demonstrates greater accuracy than in pre...

A hidden Markov model based classifier is proposed in this paper to perform automatic speech recognition using myoelectric signals from the muscles of vocal articulation. The classifier's resilience to temporal variance is compared to a linear discriminant analysis classifier that was used in a pervious study. Speech recognition was performed, usin...

Noninvasive measurements of somatosensory evoked potentials have both clinical and research applications. The electrical artifact which results from the stimulus is an interference which can distort the evoked signal, and introduce errors in response onset timing estimation. Given that this interference is synchronous with the evoked signal, it can...

This work represents ongoing investigation in pattern recognition for myoelectric control. It is shown that four channels of myoelectric data greatly improve the classification accuracy, as compared to two channels. Also, it is demonstrated that the steady-state myoelectric signal may be classified with greater accuracy than the transient signal. T...

A new technique to extract more control information from the myoelectric signal (MES) is introduced. The technique is based on the correlation of the MES obtained from a linear array of surface electrodes. The goal is to develop a continuous classifier of the MES to be used for myoelectric control

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

In January 1996, the Working Group 25 of the former AGARD Aerospace Medical Panel began to evaluate the potential of alternative (new and emerging) control technologies for future aerospace systems. The present report summarizes the findings of this group. Through different chapters, the various human factors issues related to the introduction of a...

Muscle activity produces an electrical signal termed the myo-electric signal (MES). The MES is a useful clinical tool, used in diagnostics and rehabilitation. This signal is typically stored in 2 bytes as 12-bit data, sampled at 3 kHz, resulting in a 6 kbyte s-1 storage requirement. Processing MES data requires large bit manipulations and heavy mem...

An accurate and computationally efficient means of classifying myoelectric signal (MES) 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 MES pattern classification, many forms of signal representati...

A method for classifying movement patterns of the upper arm, intended for multifunction control of arm prostheses, is presented. A finite impulse response neural network (FIRNN) is trained on 100 msec segments of myoelectric signals (MES) recorded during the very initial stage of elbow flexion (FL) and extension (EX). The network develops a clear i...

Somatosensory evoked potentials (SEPs) are useful in evaluating the integrity and determining the physiological parameters of the nervous system. Several methods have been proposed to reduce the interference in SEP measurements. Synchronous averaging is useful to reduce random interference, and adaptive methods have been developed to reduce the sti...

The recording of somatosensory evoked potentials (SEP) is very important today in diagnostic and intraoperative procedures. Ensemble averaging improves the signal-to noise ratio (SNR) by reducing the random interference. Ensemble averaging does not reduce the stimulus artifact which tends to mask or at least distort the SEP. The artifact is a resul...

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 implicitl...

The identification of physical signals is key to many signal processing applications. In the last decade, artificial neural networks have been shown to be a powerful tool for such pattern recognition tasks. Many signals are transient in nature, that is, they exist for only a limited duration in time. Moreover, much of the information in these trans...

This paper describes the design and development of an eight-channel ambulatory monitor for use in ergonomic and rehabilitation studies. This portable, battery operated instrument is capable of recording various physiological and biomechanical parameters during the course of a working shift. By incorporating a digital signal processor within the ins...

The enhancement of an existing myoelectric control system has been investigated. The original one-channel system used an artificial neural network to classify myoelectric patterns. This research shows that a two-channel control system can improve the classification accuracy of the pattern classifier significantly, thus improving the reliability of...

Bend enhanced fiber (BEF) sensors are curvature-measuring optical analogs of elongation- measuring resistance strain gauges. They are made by treating optical fibers to have an optically absorptive zone along a thin axial stripe a few millimeters long. Light transmission through the fiber past this zone then becomes a robust function of curvature,...

Recent work by Hudgins (1993) has proposed a neural network-based approach to classifying the myoelectric signal (MES) elicited at the onset of movement of the upper limb. A standard feedforward artificial network was trained (using the backpropagation algorithm) to discriminate amongst four classes of upper-limb movements from the MES, acquired fr...

This paper describes a novel approach to the control of a multifunction prosthesis based on the classification of myoelectric patterns. 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 p...

First Page of the Article

First Page of the Article

Significant, reproducible changes occur in the surface myoelectric power spectral density as a function of muscle force. The power spectrum shifted to higher frequencies as the muscle force increased through most of the contraction range. At high muscle force levels, no evidence was found that would indicate the occurrence of synchronous firing of...

Without Abstract

Techniques for the estimation of skeletal muscle fiber conduction velocity are of considerable interest. These techniques use, in general, some form of cross correlation or zero-crossing analysis. Cross correlation is a straightforward method of conduction velocity estimation, however, it is difficult to realize low-cost real-time processors. Polar...

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’...

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 t...

Introduction Myoelectric prostheses are well accepted by below elbow amputees but less well by those with higher level amputations. The primary limitation at present lies in the control system. Although these systems have been successful for single device control (hand or elbow), the extension to the control of more than one device (either simultan...

An accurate and computationally efficient means of classifying myoelectric signal (MES) 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 MES pattern classification, many forms of signal representati...