Electromyography: Physiology, Engineering, and Non-Invasive Applications / Edition 1

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Overview

A complete overview of electromyography with contributions from pacesetters in the field

In recent years, insights from the field of engineering have illuminated the vast potential of electromyography (EMG) in biomedical technology. Featuring contributions from key innovators working in the field today, Electromyography reveals the broad applications of EMG data in areas as diverse as neurology, ergonomics, exercise physiology, rehabilitation, movement analysis, biofeedback, and myoelectric control of prosthesis.

Bridging the gap between engineering and physiology, this pioneering volume explains the essential concepts needed to detect, understand, process, and interpret EMG signals using non-invasive electrodes. Electromyography shows how engineering tools such as models and signal processing methods can greatly augment the insight provided by surface EMG signals.

Topics covered include:

  • Basic physiology and biophysics of EMG generation
  • Needle and surface electrode detection techniques
  • Signal conditioning and processing issues
  • Single- and multi-channel techniques for information extraction
  • Development and application of physical models
  • Advanced signal processing techniques
With its fresh engineering perspective, Electromyography offers physiologists, medical professionals, and students in biomedical engineering a new window into the far-reaching possibilities of this dynamic technology.
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Editorial Reviews

Doody's Review Service
Reviewer: Celso Agner, MD, MS, MSc (Michigan Neurology Partners)
Description: Electromyography is one of the major pillars of neurology and helps determine the diagnosis of multiple neurological conditions. The use of non-invasive neurophysiologic methods allows for a perfect understanding of the physiology of multiple neurological diseases.
Purpose: The purpose of this book is to expose the unknown aspects of electromyography, in particular the non-invasive approaches to the neurophysiological monitoring in the central nervous system. In my view, those were fully achieved in the book. The authors successfully reported the worthy objectives in this book.
Audience: Neurologists and engineers are the main targets for this book. In my view, however, the target audience can be extended to neurosurgeons and neurophysiologists working with conditions such as epilepsy or functional neurosurgery, where it is necessary to understand the brain physiology in vivo.
Features: The authors contribute eighteen chapters in this book. The text progresses from deep scientific information on the physiology of electrical impulses to the clinical applications in medicine. As compared to other books with similar topics, this reflects the potential applications of EMG techniques into fields like ergonomics, rehabilitation, and neurology. The most interesting aspect is the potential improvement in rehabilitation potentials in patients with diverse neurological conditions, a problem faced by many neurologists, neurosurgeons, and other clinicians alike. The pictures are opportune, the text easily read, and the price adequate for the quality of the book.
Assessment: This is an important book for general libraries and, in particular, neurosciences libraries where electrophysiology is a major component of the diagnostic armamentarium.
From the Publisher
"...the best single reference book currently available in the field…" (Annals of Biomedical Engineering, November 2005)

"...the authors successfully reported...the unknown aspects of electromyography, in particular the non-invasive approaches to the neurophysiological monitoring of the central nervous system." (Doody's Health Services)


3 Stars from Doody
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Product Details

Meet the Author

Roberto Merletti, PhD, is Director of the Laboratory for Engineering of the Neuromuscular System at Politecnico di Torino, Italy, and coordinator of neuromuscular research projects for the European Community and European Space Agency. He is author or coauthor of two books and more than fifty journal articles and received his doctoral degree from The Ohio State University.

Philip A. Parker, PhD, is Professor of Electrical Engineering at the University of New Brunswick, Canada, where he received his doctoral degree. He has authored or coauthored three book chapters and more than fifty journal articles.

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Read an Excerpt

Electromyography

Physiology, Engineering, and Noninvasive Applications

John Wiley & Sons

Copyright © 2004 Institute for Electrical and Electronics Engineers, Inc.
All right reserved.

ISBN: 0-471-67580-6


Chapter One

BASIC PHYSIOLOGY AND BIOPHYSICS OF EMG SIGNAL GENERATION

T. Moritani

Laboratory of Applied Physiology The Graduate School of Human and Environmental Studies Kyoto University, Kyoto, Japan

D. Stegeman

Department of Clinical Neurophysiology University Medical Center, Nijmegen Interuniversity Institute for Fundamental and Clinical Human Movement Sciences (IFKB) Amsterdam, Nijmegen, The Netherlands

R. Merletti

Laboratory for Engineering of the Neuromuscular System Department of Electronics Politecnico di Torino, Italy

1.1 INTRODUCTION

Understanding EMG signals implies the understanding of muscles and the way they generate bioelectrical signals. It also implies the understanding of the "forward problem," that is, how specific mechanisms and phenomena influence the signals, as well as the more difficult "inverse problem", that is, how the signals reflect certain mechanisms and phenomena and allow their identification and description. The concept of forward and inverse problem is familiar to physiologists and engineers and is strictly associated to the concept of a system as a set of inputs, transfer functions and outputs, and of a model, as a set of descriptions and relations associating, under certain conditions and assumptions, the inputs to the outputs.

In this chapter we provide a basic description of the physiological system whose output is the needle or surface detected EMG signal. We summarize the large number of factors and phenomena that contribute to such signals and provide a basis of knowledge for the signal analysis approaches that will be addressed in the subsequent chapters. In Section 1.2 we introduce basic concepts and mechanisms of muscle physiology and motor control. In Section 1.3 we consider the basic electrophysiology of the muscle membrane. We assume that some of the concepts described in Section 1.2 (action potential, power spectrum, etc.) are known to the reader. They are discussed in greater detail in Section 1.3 and in other chapters of this book.

1.2 BASIC PHYSIOLOGY OF MOTOR CONTROL AND MUSCLE CONTRACTION

1.2.1 Motor Unit

The human motor system must cope with a great diversity of internal and external demands and constraints. These include the regulation of force output for precise and powerful movements, upright posture, locomotion, and even our repertoire of gestures. As it is impossible to describe all the specific control features of the various motor systems in isolation, we will attempt to delineate the basic principles of motor control, with special attention to the skeletomotor system, which plays the major role in the control of force and movements in humans.

Simplified schematic diagrams of the central motor system and the concept of the motor unit (MU are presented in Figure 1.1). The central nervous system is organized in a hierarchical fashion. Motor programming takes place in the premotor cortex, the supplementary motor area, and other associated areas of the cortex. Inputs from these areas, from the cerebellum and, to some extent, from the basal ganglia converge to the primary motor cortex and excite or inhibit the various neurons of the primary motor cortex. The outputs from the primary motor cortex have a powerful influence on interneurons and motoneurons of the brain stem and of the spinal cord. There exists a link between the corticospinal tract and alpha ([alpha])-motoneurons, providing direct cortical control of muscle activity, as indicated in Figure 1.1.

A motor unit (MU) consists of an [alpha]-motoneuron in the spinal cord and the muscle fibers it innervates (Fig. 1.1). The [alpha]-motoneuron is the final point of summation for all the descending and reflex input. The net membrane current induced in this motoneuron by the various synaptic innervation sites determines the discharge (firing) pattern of the motor unit and thus the activity of the MU. The number of MUs per muscle in humans may range from about 100 for a small hand muscle to 1000 or more for large limb muscles. It has also been shown that different MUs vary greatly in force generating capacity, with a 100-fold or more difference in twitch force.

The wide variation in the morphological and electrophysiological properties of the individual motoneurons comprising a motoneuron pool is matched by an equally wide range in the physiological properties of the muscle units they innervate. Interestingly the muscle fibers that are innervated by a particular motoneuron manifest nearly identical biochemical, histochemical, and contractile characteristics, together defining the typing of the specific MU. Earlier studies identified three types of motor units based on physiological properties such as speed of contraction and fatigability (sensitivity to fatigue): (1) fast-twitch, fatigable (FF or type IIb); (2) fast-twitch, fatigue-resistant (FR or type IIa); and (3) slow-twitch (S or type I), which is most resistant to fatigue. The FF type motor units are predominantly found in pale muscles (high ATPase enzyme for anaerobic energy utilization), low capillarization, less hemoglobin, myoglobin, and mitochondria for oxidative energy supply), while red muscles (low ATPase, high capillarization, abundant hemoglobin, myoglobin and mitochondria for oxidative energy supply) such as the soleus are predominantly composed of type S motor units.

Figure 1.2 shows typical contractile properties of predominantly fast-twitch (extensor digitorum longus, EDL) and slow-twitch (soleus, SOL) fibers obtained from an isolated rat muscle. Note the large differences in contractile force, contraction time (CT), electromechanical delay time (EMD), and maximal rate of force development (dF/dt) and relaxation.

In humans a classification of motor units based on their physiological properties is difficult to achieve. Therefore an identification of muscle fiber populations in the muscle cross section based on histochemical criteria has been commonly adopted after obtaining a small sample of muscle tissue by a needle biopsy technique. Type I muscle fibers have high levels of ATPase activity and low levels of succinic dehydrogenase (SDH, one of the major enzymes for aerobic energy production), and type II fibers demonstrate the reverse pattern of enzyme activity. Type II fibers are subdivided in two subgroups type IIa and type IIb with different properties. Figure 1.3 shows histochemical fiber typing in human skeletal muscle demonstrating different myofibrillar ATPase reactions after preincubation at pH 4.6. In this preparation, type I (slow-twitch) fibers stain dark, type IIa fibers remain unstained, and type IIb fibers moderately stained (see Fig. 1.3).

During aerobic work glycolytic energy metabolism furnishes pyruvate, which is then transferred to the mitochondria where its carbon skeleton is entirely degraded to C[O.sub.2] through oxidative phosphorylation. This process of full oxidation of glucose in mitochondria yields 36 ATP molecules for each glucose molecule degraded. Note that anaerobic glycolysis of glucose to pyruvate only yields 2 ATP with a subsequent formation of lactic acid, which may affect muscle contractile activity. Thus the net ATP production differs by a factor 18 between aerobic and anaerobic energy metabolism. Consequently type I fibers are fatigue resistant due to their high oxidative metabolism and their higher energy efficiency.

Type II (fast-twitch) fibers stain weaker for succinic dehydrogenase than type I fibers, but stain stronger for the enzymes necessary for anaerobic metabolism. Type II fibers therefore generate the ATP for muscular contraction mainly through anaerobic glycolysis, which results in the production of lactic acids and other metabolic by-products. They possess small amounts of mitochondria, and their power output during repetitive activation cannot be achieved through ATP production by oxidative process in their mitochondria. Thus type II fibers are prone to fatigue quickly because they accumulate lactic acids (up to 30-fold the concentration in resting muscle). The low pH associated with this lactate accumulation, as well as the corresponding increases in free phosphate and other metabolic byproducts, inhibits the chemical reactions including the myosin ATPase, slowing contraction speed or stopping active contraction entirely (see Fig 1.4). The different metabolic pathways are activated depending on the speed, the intensity, and the duration of muscular contraction.

In addition the MU type is not only reflected in mechanical and histological differences but also in the single-fiber action potential and in the MU action potential features. Wallinga et al. investigated the action potential of individual muscle fibers of the rat soleus (type I) and the extensor digitorum longus (EDL, predominantly type II). They found that in comparison to type I fibers, type II fibers have more negative resting potential, larger peak excursion, faster rate of depolarization and repolarization and shorter action potential duration. Furthermore type I and types IIa and IIb muscle fibers appear to be randomly distributed across the muscle cross section. Depending on the muscle function, the percentage of the two fiber types may be different. Antigravity muscles (e.g., soleus) tend to be predominantly type I, while muscles suitable for rapid movements have similar proportions of the two fiber types. Table 1.1 summarizes different MU properties.

1.2.2 Motor Unit Recruitment and Firing Frequency (Rate Coding)

In voluntary contractions, force is modulated by a combination of MU recruitment and changes in MU activation frequency (rate coding). The greater the number of MUs recruited and their discharge frequency, the greater the force will be. During full MU recruitment the muscle force, when activated at any constant discharge frequency, is approximately 2 to 5 kg/[cm.sup.2], and in general, this is relatively independent of species, gender, age, and training status.

Our current understanding of motor unit recruitment is based on the pioneer work of Henneman and colleagues in the 1960s, who proposed that motor units are always recruited in order of increasing size of the [alpha]-motoneuron. This "size principle" of Henneman et al. was based on results from cat motoneurons and is supported by strong evidence that in muscle contraction there is a specific sequence of recruitment in order of increasing motoneuron and motor unit (MU) size. Goldberg and Derfler later showed positive correlations among recruitment order, spike amplitude, and twitch tension of single MUs in human masseter muscle. Because of the great wealth of data supporting this size-based recruitment order in a variety of experimental conditions, it is often referred to as the "normal sequence of recruitment" or "orderly recruitment". Recent data further confirm the presence of this "size principle," and that transcortical stimulation generates normal orderly recruitment.

It is well documented that motor unit recruitment and firing frequency (rate coding) depend primarily on the level of force and the speed of contraction. When low-threshold MUs are recruited, this results in a muscular contraction characterized by low force-generating capabilities and high fatigue resistance. With requirements for greater force and/or faster contraction, high-threshold fatigable MUs are recruited. The technical difficulties associated with single motor unit recordings at high forces in humans and the difficulty in generating controlled forces in animal preparations limit the accuracy with which the precise motor unit recruitment and rate coding can be established. However, Kukulka and Clamann and Moritani et al. demonstrated in human adductor pollicis that for a muscle group with mainly type I fibers, rate coding plays a prominent role in force modulation. For a muscle group composed of both types I and II fibers, MU recruitment seems to be the major mechanism for generating extra force above 40% to 50% of maximal voluntary contraction (MVC). Thus, in the intrinsic muscles of human hands, motor unit recruitment appears to be essentially complete at about 50% of maximal force, but recruitment in the biceps, brachialis, and deltoid muscles may continue until more than 80% of maximal force is attained.

The number of MUs recruited and their mean discharge frequency of excitation determine the electrical activity in a muscle, that is, there are the same factors that determine muscle force. Thus a direct relationship between the electromyogram (EMG) and exerted force might be expected. Under certain experimental conditions this relationship can be demonstrated by recording the smoothed rectified or integrated EMG (iEMG). The reproducibility of EMG recordings is remarkably high, as the test-retest correlation ranges from 0.97 to 0.99.

Figure 1.5 represents a typical set of raw surface EMG recording together with the corresponding force curve during force-varying isometric muscle contraction. Surface EMG frequency power spectral data are also shown. It can be readily seen that EMG activity increases progressively as a function of force generated, suggesting a gradual MU recruitment and MU firing rate modulation taking place in order to match the required force demand. Thus the increase in EMG amplitude might represent MU recruitment and/or MU firing frequency modulation whereas the increase of mean frequency (MPF) of the power spectrum might represent, at least in part, the additional recruitment of superficial high threshold MUs that most likely possess large and sharp spikes affecting high frequency bands of the surface EMG power spectrum.

However, the change in the surface EMG should not automatically be attributed to changes in either MU recruitment or MU firing frequencies as the EMG signal amplitude is further influenced by the individual muscle fiber potential, degree of MU discharge synchronization, and fatigue. A direct single motor unit recording with bipolar wire electrodes is shown for comparison (Fig. 1.6) during the same experimental condition previously described. Note that the isolated MU spikes can be observed. Additional motor units as represented by greater spike amplitudes could be identified even at near 80% of maximal force production.

Several previous studies have demonstrated that the firing rates of active motor units increase monotonically with increasing force output. This may imply that increased excitation to the active muscle motoneuron pool increases the firing rates of all the active motor units. In addition to this common increase, common fluctuations of firing rates are often present. De Luca et al. investigated this commonality in the fluctuations of the firing rates of up to eight concurrently active motor units during various types of isometric muscle action: attempted constant force, ramp force increase, and force reversals. Their results strongly indicated that there was a unison behavior of the firing rates of motor units, both as a function of time and force. This property has been termed the "common drive." The existence of this common drive implies that the nervous system does not control the firing rates of motor units individually. Instead, it modulates the pool of motoneurons in a uniform fashion; a demand for force modulation can be achieved by modulation of the excitation and/or inhibition on the motoneuron pool as a whole.

(Continues...)



Excerpted from Electromyography Copyright © 2004 by Institute for Electrical and Electronics Engineers, Inc.. Excerpted by permission.
All rights reserved. No part of this excerpt may be reproduced or reprinted without permission in writing from the publisher.
Excerpts are provided by Dial-A-Book Inc. solely for the personal use of visitors to this web site.

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Table of Contents

Introduction.

Contributors.

1 BASIC PHYSIOLOGY AND BIOPHYSICS OF EMG SIGNAL GENERATION (T. Moritani, D. Stegeman, R. Merletti).

1.1 Introduction.

1.2 Basic Physiology of Motor Control and Muscle Contraction.

1.3 Basic Electrophysiology of the Muscle Cell Membrane.

References.

2 NEEDLE AND WIRE DETECTION TECHNIQUES (J. V. Trontelj, J. Jabre, M. Mihelin).

2.1 Anatomical and Physiological Background of Intramuscular Recording.

2.2 Recording Characteristics of Needle Electrodes.

2.3 Conventional Needle EMG.

2.4 Special Needle Recording Techniques.

2.5 Physical Characteristics of Needle EMG Signals.

2.6 Recording Equipment.

References.

3 DECOMPOSITION OF INTRAMUSCULAR EMG SIGNALS (D. W. Stashuk, D. Farina, K. Søgaard).

3.1 Introduction.

3.2 Basic Steps for EMG Signal Decomposition.

3.3 Evaluation of Performance of EMG Signal Decomposition Algorithms.

3.4 Applications of Results of the Decomposition of an Intramuscular EMG Signal.

3.5 Conclusions.

References.

4 BIOPHYSICS OF THE GENERATION OF EMG SIGNALS (D. Farina, R. Merletti, D. F. Stegeman).

4.1 Introduction.

4.2 EMG Signal Generation.

4.3 Crosstalk.

4.4 Relationships between Surface EMG Features and Developed Force.

4.5 Conclusions.

References.

5 DETECTION AND CONDITIONING OF THE SURFACE EMG SIGNAL (R. Merletti, H. Hermens).

5.1 Introduction.

5.2 Electrodes: Their Transfer Function.

5.3 Electrodes: Their Impedance, Noise, and dc Voltages.

5.4 Electrode Configuration, Distance, Location.

5.5 EMG Front-End Amplifiers.

5.6 EMG Filters: Specifications.

5.7 Sampling and A/D Conversion.

5.8 European Recommendations on Electrodes and Electrode Locations.

References.

6 SINGLE-CHANNEL TECHNIQUES FOR INFORMATION EXTRACTION FROM THE SURFACE EMG SIGNAL (E. A. Clancy, D. Farina, G. Filligoi).

6.1 Introduction.

6.2 Spectral Estimation of Deterministic Signals and Stochastic Processes.

6.3 Basic Surface EMG Signal Models.

6.4 Surface EMG Amplitude Estimation.

6.5 Extraction of Information in Frequency Domain from Surface EMG Signals.

6.6 Joint Analysis of EMG Spectrum and Amplitude (JASA).

6.7 Recurrence Quantification Analysis of Surface EMG Signals.

6.8 Conclusions.

References.

7 MULTI-CHANNEL TECHNIQUES FOR INFORMATION EXTRACTION FROM THE SURFACE EMG (D. Farina, R. Merletti, C. Disselhorst-Klug).

7.1 Introduction.

7.2 Spatial Filtering.

7.3 Spatial Sampling.

7.4 Estimation of Muscle-Fiber Conduction Velocity.

7.5 Conclusions.

References.

8 EMG MODELING AND SIMULATION (D. F. Stegeman, R. Merletti, H. J. Hermens).

8.1 Introduction.

8.2 Phenomenological Models of EMG.

8.3 Elements of Structure-Based SEMG Models.

8.4 Basic Assumptions.

8.5 Elementary Sources of Bioelectric Muscle Activity.

8.6 Fiber Membrane Activity Profiles, Their Generation, Propagation, and Extinction.

8.7 Structure of the Motor Unit.

8.8 Volume Conduction.

8.9 Modeling EMG Detection Systems.

8.10 Modeling Motor Unit Recruitment and Firing Behavior.

8.11 Inverse Modeling.

8.12 Modeling of Muscle Fatigue.

8.13 Other Applications of Modeling.

8.14 Conclusions.

References.

9 MYOELECTRIC MANIFESTATIONS OF MUSCLE FATIGUE (R. Merletti, A. Rainoldi, D. Farina).

9.1 Introduction.

9.2 Definitions and Sites of Neuromuscular Fatigue.

9.3 Assessment of Muscle Fatigue.

9.4 How Fatigue Is Reflected in Surface EMG Variables.

9.5 Myoelectric Manifestations of Muscle Fatigue in Isometric Voluntary Contractions.

9.6 Fiber Typing and Myoelectric Manifestations of Muscle Fatigue.

9.7 Factors Affecting Surface EMG Variable.

9.8 Repeatability of Estimates of EMG Variables and Fatigue Indexes.

9.9 Conclusions.

References.

10 ADVANCED SIGNAL PROCESSING TECHNIQUES (D. Zazula, S. Karlsson, C. Doncarli).

10.1 Introduction.

10.2 Theoretical Background.

10.3 Decomposition of EMG Signals.

10.4 Applications to Monitoring Myoelectric Manifestations of Muscle Fatigue.

10.5 Conclusions.

Acknowledgment.

References.

11 SURFACE MECHANOMYOGRAM (C. Orizio).

11.1 The Mechanomyogram (MMG): General Aspects during Stimulated and Voluntary Contraction.

11.2 Detection Techniques and Sensors Comparison.

11.3 Comparison between Different Detectors.

11.4 Simulation.

11.5 MMG Versus Force: Joint and Adjunct Information Content.

11.6 MMG Versus EMG: Joint and Adjunct Information Content.

11.7 Area of Application.

References.

12 SURFACE EMG APPLICATIONS IN NEUROLOGY (M. J. Zwarts, D. F. Stegeman, J. G. van Dijk).

12.1 Introduction.

12.2 Central Nervous System Disorders and SEMG.

12.3 Compound Muscle Action Potential and Motor Nerve Conduction.

12.4 CMAP Generation.

12.5 Clinical Applications.

12.6 Pathological Fatigue.

12.7 New Avenues: High-Density Multichannel Recording.

12.8 Conclusion.

References.

13 APPLICATIONS IN ERGONOMICS (G. M. Hägg, B. Melin, R. Kadefors).

13.1 Historic Perspective.

13.2 Basic Workload Concepts in Ergonomics.

13.3 Basic Surface EMG Signal Processing.

13.4 Load Estimation and SEMG Normalization and Calibration.

13.5 Amplitude Data Reduction over Time.

13.6 Electromyographic Signal Alterations Indicating Muscle Fatigue in Ergonomics.

13.7 SEMG Biofeedback in Ergonomics.

13.8 Surface EMG and Musculoskeletal Disorders.

13.9 Psychological Effects on EMG.

References.

14 APPLICATIONS IN EXERCISE PHYSIOLOGY (F. Felici).

14.1 Introduction.

14.2 A Few “Tips and Tricks”.

14.3 Time and Frequency Domain Analysis of sEMG: What Are We Looking For?

14.4 Application of sEMG to the Study of Exercise.

14.5 Strength and Power Training.

14.6 Muscle Damage Studied by Means of sEMG.

References.

15 APPLICATIONS IN MOVEMENT AND GAIT ANALYSIS (C. Frigo, R. Shiavi).

15.1 Relevance of Electromyography in Kinesiology.

15.2 Typical Acquisition Settings.

15.3 Study of Motor Control Strategies.

15.4 Investigation on the Mechanical Effect of Muscle Contraction.

15.5 Gait Analysis.

15.6 Identification of Pathophysiologic Factors.

15.7 Workload Assessment in Occupational Biomechanics.

15.8 Biofeedback.

15.9 The Linear Envelope.

15.10 Information Enhancement through Multifactorial Analysis.

References.

16 APPLICATIONS IN REHABILITATION MEDICINE AND RELATED FIELDS (A. Rainoldi, R. Casale, P. Hodges, G. Jull).

16.1 Introduction.

16.2 Electromyography as a Tool in Back and Neck Pain.

16.3 EMG of the Pelvic Floor: A New Challenge in Neurological Rehabilitation.

16.4 Age-Related Effects on EMG Assessment of Muscle Physiology.

16.5 Surface EMG and Hypobaric Hipoxia.

16.6 Microgravity Effects on Neuromuscular System.

References.

17 BIOFEEDBACK APPLICATIONS (J. R. Cram).

17.1 Introduction.

17.2 Biofeedback Application to Impairment Syndromes.

17.3 SEMG Biofeedback Techniques.

17.4 Summary.

References.

18 CONTROL OF POWERED UPPER LIMB PROSTHESES (P. A. Parker, K. B. Englehart, B. S. Hudgins).

18.1 Introduction.

18.2 Myoelectric Signal as a Control Input.

18.3 Conventional Myoelectric Control.

18.4 Emerging MEC Strategies.

18.5 Summary.

References.

Index.

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