Surface Electromyography (eBook)

Surface Electromyography: Physiology, Engineering, and Applications 1
Contents 7
Introduction 9
Outline of the Book 10
Open Technical and Scientific Issues 11
Education, Training, and Standardization in the Field of Emg Imaging 13
Final Remarks 13
Acknowledgments 15
Contributors 17
Chapter 1: Physiology of Muscle Activation and Force Generation 21
1.1 Introduction 21
1.2 Anatomy of a Motor Unit 21
1.2.1 Motor Nucleus 22
1.2.2 Muscle Fibers 22
1.3 Motor Neuron 25
1.4 Muscle Unit 27
1.4.1 Muscle Fiber Action Potentials 28
1.4.2 Muscle Unit Force 29
1.4.3 Motor Unit Types 32
1.5 Recruitment and Rate Coding 35
1.5.1 Orderly Recruitment 35
1.5.2 Rate Coding 37
1.5.3 Discharge Rate Patterns 39
1.6 Summary 42
References 43
Chapter 2: Biophysics of the Generation of EMG Signals 50
2.1 Introduction 50
2.2 EMG Signal Generation 51
2.2.1 Signal Source 51
2.2.2 Generation and Extinction of the Intracellular Action Potential 54
2.2.3 Volume Conductor 56
2.2.4 EMG Detection, Electrode Montages, and Electrode Size 58
2.3 Anatomical, Physical, and Detection System Parameters Influencing EMG Features 61
2.4 Crosstalk 62
2.4.1 Crosstalk and Detection System Selectivity 62
2.5 EMG Amplitude and Force 65
2.6 Conclusion/Summary 69
References 69
Chapter 3: Detection and Conditioning of Surface EMG Signals 74
3.1 Introduction 74
The Single Electrode-Skin Interface, Its Impedance and Noise 75
Sensitivity to Power Line Interference 75
Balancing the Contact Impedances 76
The Transfer Function of the Electrode System 76
3.2 The Electrode-Skin Interface and the Front-End Amplifier Stage 76
3.2.1 Electrode-Skin Impedance 76
3.2.2 Effects of Skin Treatment on Impedance and Noise 83
Impedance 83
Noise 85
3.2.3 Capacitive Electrodes 87
3.2.4 The Transfer Function of the Electrode System 89
3.3 State of the Art on Emg Signal Conditioning and Interfacing Solutions 92
3.3.1 General Device Features and Specifications 92
3.3.2 High-Performance Instrumentation Amplifiers and Acquisition Systems on the Market 95
3.4 Asic Solutions on the Market 104
3.4.1 IMEC 104
3.4.2 Intan Technologies, LLC 104
3.4.3 Texas Instruments 105
3.5 Perspectives for the Future 106
References 106
Chapter 4: Single-Channel Techniques for Information Extraction From the Surface EMG Signal 111
4.1 Introduction 111
4.2 Spectral Estimation of Deterministic Signals and Stochastic Processes 113
4.2.1 Fourier-Based Spectral Estimators 113
4.2.2 Parametric-Based Spectral Estimators 114
4.2.3 Estimation of the Time-Varying PSD of Nonstationary Stochastic Processes 116
4.3 Basic Surface EMG Signal Models 116
4.4 Surface Emg Amplitude Estimation 119
4.4.1 Measures of Amplitude Estimator Performance 120
4.4.2 EMG Amplitude Processing-Overview 121
Stage 1: Noise and Interference Attenuation 121
Stage 2: Whitening 121
Stages 3 and 5: Demodulation and Relinearization 123
Stage 4: Smoothing 124
4.4.3 Applications of EMG Amplitude Estimation 124
4.5 Extraction of Information in the Frequency Domain From Surface EMG Signals 125
4.5.1 Estimation of the PSD of the Surface EMG Signal Detected During Voluntary Contractions 126
4.5.2 The Energy Spectral Density of the Surface EMG Signal Detected During Electrically Elicited Contractions 126
4.5.3 Descriptors of Spectral Compression 128
Properties of MNF and MDF 131
Fourier Versus Parametric Approach 131
Window Shape 131
Epoch Length and Epoch Overlapping in the Case of Stationary and Nonstationary Conditions 133
4.5.4 Other Approaches for Detecting Changes in Surface EMG Frequency Content During Voluntary Contractions 133
4.5.5 Applications of Spectral Analysis of the Surface EMG Signal 135
4.5.6 Correlation and Coherence Analysis of EMG Signals 136
4.6 Conclusions 138
References 139
Chapter 5: Techniques for Information Extraction From the Surface EMG Signal: High-Density Surface EMG 146
5.1 Introduction 146
5.2 Spatial Distribution of EMG Potential and Emg Features in Muscles With Fibers Parallel to the Skin 147
5.2.1 Spatial Distribution of Instantaneous EMG Potential in Muscles with Fibers Parallel to the Skin 147
1-D Electrode Arrays 147
2-D Electrode Arrays 150
5.2.2 Spatial Distribution of EMG Features in Muscles with Fibers Parallel to the Skin 153
5.2.3 Spatial Filtering 158
5.2.4 Estimation of Muscle Fiber Conduction Velocity 161
5.3 Spatial Distribution of Emg Potential and Features in Pinnate Muscles 164
5.3.1 Muscles with Fibers Parallel to the Skin Versus Muscle with Fibers Pinnate in the Depth Direction 164
5.3.2 Spatial Distribution of EMG Features in Muscles Pinnate in Depth Direction 168
5.4 Current Applications and Future Perspectives of Hdsemg 169
5.4.1 Surface EMG Imaging 170
5.4.2 Surface EMG Decomposition 172
5.4.3 Future Perspectives 173
References 173
Chapter 6: Muscle Coordination, Motor Synergies, and Primitives From Surface EMG 178
6.1 Introduction 178
6.2 Muscle Synergies and Spinal Maps 179
6.2.1 EMG Factorization into Muscle Synergies 180
6.2.1.1 Time-Invariant Factorization Algorithms 181
6.2.1.2 Time-Varying Factorization Algorithms 181
6.2.2 Reconstructing the Spinal Maps of Motoneuron Activation from Surface EMGs 182
6.3 Muscle Synergies in Posture Control 183
6.4 Modular Control of Arm Reaching Movements 184
6.5 Motor Primitives in Human Locomotion 187
6.5.1 Spatiotemporal Architecture of Multi-Muscle EMG Activity 187
6.5.2 Segmental Spinal Motor Output 189
6.5.3 Bilateral Coordination 192
6.5.4 Development of Locomotor Primitives 192
6.5.5 Reorganization of Motor Patterns in Movement Disorders 194
6.6 Conclusions 194
References 195
Chapter 7: Surface EMG Decomposition 200
7.1 Introduction 200
7.2 EMG Mixing Process 201
7.2.1 Redundancy in Multichannel EMG Measurements and the Required Number of EMG Channels 208
7.3 EMG Decomposition Techniques 209
7.3.1 Template Matching Approaches 210
7.3.2 Latent Component Analysis 210
7.3.2.1 Instantaneous Source Separation 212
7.3.2.2 Convolutive Source Separation 213
7.4 Validation of Decomposition 217
7.4.1 Accuracy of Motor Unit Identification 218
7.4.2 Representativeness of Identified Motor Units 220
References 222
Chapter 8: EMG Modeling and Simulation 230
8.1 Introduction 230
8.2 Principles of Modeling and Simulation 231
8.3 Phenomenological Surface EMG Models 232
8.4 Structure-Based Surface Emg Models 234
8.5 Modeling the Action Potential Source 235
8.5.1 The Intracellular Action Potential 235
8.5.2 Modeling Action Potential Propagation, Generation, and Extinction 237
8.6 Models of Volume Conduction and Detection Systems 239
8.6.1 Infinite Volume Conductor Models 240
8.6.2 Finite Volume Conductor Models 242
8.6.2.1 Analytical Finite Volume Conductor Models 243
8.6.2.2 Numerical Volume Conductor Models 243
8.6.2.3 Boundary Conditions 245
8.6.3 Material Properties 245
8.6.3.1 Tissue Conductivity and Permittivity 247
8.6.3.2 Muscle Anisotropy 247
8.6.3.3 Local Inhomogeneities 248
8.6.4 Modeling the EMG Electrode 248
8.6.5 The Motor Unit Action Potential 249
8.7 Models of the Surface EMG Signal 251
8.7.1 Modeling Motor Unit Recruitment, Firing, and Synchronization 251
8.7.1.1 Motor Unit Synchronization 252
8.7.2 Incorporating Force Generation 253
8.8 Model Validation 253
8.9 Applications of Modeling 255
8.9.1 Modeling Muscle Fatigue 255
8.9.2 Understanding Crosstalk 256
8.9.3 Modeling Pathological Conditions 256
8.10 Conclusions 256
References 258
Chapter 9: Electromyography-Driven Modeling for Simulating Subject-Specific Movement At the Neuromusculoskeletal Level 267
9.1 Introduction 267
9.2 Motion Capturing and Biomechanical Modeling of the Human Body 269
9.3 Musculoskeletal Modeling 271
9.4 EMG-Driven Musculoskeletal Modeling and Simulation 274
9.4.1 Musculotendon Kinematics 276
9.4.2 Musculotendon Activation 278
9.4.3 Musculotendon Dynamics 279
9.4.4 Joint Moment Computation 281
9.4.5 Model Calibration 281
9.5 Experimental Results and Applications 282
9.6 Conclusions 286
Acknowledgment 287
References 288
Chapter 10: Muscle Force and Myoelectric Manifestations of Muscle Fatigue in Voluntary and Electrically Elicited Contractions 293
10.1 Introduction 293
10.2 Joint Torque Measurement and Muscle Force Estimation in Isometric Contractions 294
10.2.1 Joint Torque Measurements in Isometric Contractions 294
10.2.2 Surface EMG-Based Joint Torque Estimation in Isometric Contractions 295
10.2.3 Reading sEMG for Force Estimation 299
10.3 Physiological Mechanisms of Muscle Fatigue: a Modeling Approach 301
10.4 Myoelectric Manifestations of Muscle Fatigue in Isometric, Constant Force, Voluntary Contractions 304
10.4.1 Quantification of Muscle Fatigue from sEMG in Isometric Constant Force Voluntary Contractions 305
10.5 Myoelectric Manifestations of Muscle Fatigue in Dynamic Contractions 313
10.6 Myoelectric Manifestations of Fatigue in Electrically Elicited Contractions 314
10.7 EMG Power Spectrum and Fiber-Typing a Controversial Issue
10.8 Repeatability of Measurements and Applications of Results 319
10.8.1 Repeatability of Measurements 319
10.8.2 Applications of Results 320
References 322
Chapter 11: EMG of Electrically Stimulated Muscles 331
11.1 Electrical Stimulation of the Peripheral Nervous System 331
11.1.1 Stimulation Techniques 332
11.1.2 MU Recruitment During NMES: Direct Activation of Motor Axons 334
11.1.3 MU Recruitment During NMES: Reflexive Activation of Spinal Motor Neurons 335
11.2 Surface EMG Detection During Electrical Stimulation 337
11.2.1 M-Waves and Stimulation Artifact 337
Front-End Electronics 337
Stimulator Output Stage 339
Software Techniques 339
11.2.2 Myoelectric Manifestations of Muscle Fatigue During NMES 340
11.2.3 Incremental M-Waves 343
11.2.4 Incremental Stimulation for Estimating Motor Unit Number 343
References 347
Chapter 12: Surface EMG Applications in Neurophysiology 353
12.1 Introduction 353
12.2 Surface EMG Activity 354
12.2.1 Surface EMG Variables 354
12.2.2 Normalization Procedures 355
12.2.3 Muscle Fiber Conduction Velocity 356
12.3 Evoked Potential 358
12.3.1 Electrical Stimulation 358
The Compound Muscle Action Potential-M Wave 358
Electrode Configuration 358
Electrical Nerve Stimulation and Motor Unit Recruitment Order 360
The Maximal Amplitude of the M Wave-Mmax 360
The Hoffmann Reflex 361
The H-Reflex and M-Wave Recruitment Curve 361
Stimulation Parameters 363
Factors Influencing the H Reflex 364
12.3.2 Magnetic Stimulation 364
Principle 364
Type of Coil 365
Type of Stimulation 365
MEP Variables 366
12.4 Applications 366
12.4.1 Muscle Cramps 366
12.4.2 Neuromuscular Fatigue 369
12.4.3 Strength Training 372
12.4.4 Aging 374
12.5 Conclusions 375
References 376
Chapter 13: Surface EMG in Ergonomics and Occupational Medicine 381
13.1 Introduction 381
13.2 Surface EMG in Ergonomics and Occupational Medicine 382
13.3 Basic Workload Concepts and Technical Issues 383
13.4 EMG-Force Relationship 385
13.4.1 A Word of Caution 385
13.4.2 Techniques for Estimating Muscle Force or Joint Torque from sEMG 387
Average Rectified Value (ARV) and Root Mean Square (RMS) 387
Single or Multichannel Adaptive Whitening 387
High-Pass Filtering of Surface EMG Signals 388
Bayesian Filtering 388
Principal Component Analysis (PCA) and Independent Component Analysis (ICA) 388
MUAP Rate (MR) 388
Activity Index (AI) 388
13.5 Dose and Exposure in Ergonomics 389
13.5.1 The Concept of Exposure 389
13.5.2 Exposure Variation Analysis (EVA) 389
13.5.3 Gap Analysis 390
13.5.4 Joint Analysis of Spectrum and Amplitude (JASA) 391
13.6 Normalization Models 392
13.7 High-Density Emg Recordings in Ergonomics 392
13.8 Examples of Applications 393
13.8.1 sEMG and Force Perception 393
13.8.2 Surface EMG Topographical Changes and Fatigue 395
13.8.3 Simulation of Precision Industrial Work 396
13.8.4 Playing-Related Musculoskeletal Disorders 398
13.9 Conclusions 403
References 403
Chapter 14: Applications in Proctology and Obstetrics 412
14.1 Introduction 412
14.2 EMG and Innervation of the External Anal Sphincter 414
14.3 EMG and Innervation of the Puborectalis Muscle 418
14.4 Modeling of the EMG of the Anal Sphincter 419
14.5 Child-Delivery-Related Lesions and Eas Denervation 421
References 424
Chapter 15: EMG and Posture in its Narrowest Sense 428
15.1 Introduction 428
15.2 EMG and Natural Standing 429
15.2.1 What Control Mechanisms Have Been Inferred from Studying EMG in Standing? 430
15.3 EMG and Postural Perturbations 432
15.3.1 Which and How Muscles Are Activated in Response to Postural Perturbations? 432
15.3.2 Alterations in EMG Responses with Dementias 434
15.3.3 Internal Sources of Postural Perturbations 436
15.3.4 Are the Mechanisms Involved in the Control of Perturbed and Natural Standing the Same? 436
15.3.5 What Are the Characteristics and What Is the Goal of the Feedback System Involved in Standing Control? 437
15.4 New Physiological and Postural Insights Gained From Gastrocnemius, High-Density Surface Electromyograms 438
15.4.1 Interpreting Surface EMGs from Gastrocnemius Muscles 439
15.4.2 Estimating the Territory Size of Postural Motor Units from High-Density Surface EMGs 446
15.4.3 Is the Human Gastrocnemius Muscle Compartmentalized? What Is the Relevance of Compartments in Standing? 452
References 453
Chapter 16: Applications in Movement and Gait Analysis 460
16.1 The Relevance of Electromyography in Kinesiology 460
16.2 Experimental Setting and Semg in Motion Analysis 463
16.3 Semg-Based Information Used in Applications of Motion Analysis 469
16.4 Examples of Applications in Motion Analysis 472
16.5 Conclusions and Perspectives 474
References 475
Chapter 17: Applications in Musculoskeletal Physical Therapy 480
17.1 Introduction 480
17.2 Timing of Muscle Activity 481
17.2.1 Delayed Onset of Muscle Activity 481
17.2.2 Delayed Offset 483
17.3 Myoelectric Manifestations of Muscle Fatigue 484
17.4 Amplitude of Muscle Signals 488
17.5 Surface Emg Tuning Curves 491
17.6 Distribution of Muscle Activity 492
17.7 Monitoring Change With Rehabilitation 495
17.8 Conclusions 495
References 496
Chapter 18: Surface EMG Biofeedback 505
18.1 The Beginnings and Principles of Biofeedback 505
18.2 sEMG Biofeedback 507
18.3 sEMG-Biofeedback Applications and Considerations 507
18.3.1 Is Electrode Placement Important? 507
18.3.2 Which sEMG Index Should Be Used? 508
18.3.3 How Should the Information Be Fed Back? 509
18.3.4 How Often and When Is It Appropriate to Give Feedback? 510
18.4 sEMG Biofeedback: Clinical Applications 511
18.4.1 Stroke 511
18.4.2 Cerebral Palsy 512
18.4.3 Spinal Cord Injury 512
18.4.4 Knee Conditions 513
18.4.5 Low Back and Neck Pain 513
18.4.6 Urinary Incontinence 514
18.4.7 Temporomandibular Disorders and Headache 514
18.5 Future Perspectives 515
18.6 Conclusion 516
References 517
Chapter 19: EMG in Exercise Physiology and Sports 521
19.1 Surface EMG for Studying Muscle Coordination 521
19.1.1 Methodological Issues in Assessing Muscle Coordination 521
Filtering EMG 522
EMG Normalization 522
Assessing Timing Activation 522
19.1.2 Co-activation 523
Role of Co-activation 523
Surface EMG Variables to Assess Co-activation 523
Motor Learning and Co-activation 523
Task Differences 524
Age Effect 524
19.1.3 Onset Timing 524
Preparatory Activity 525
Latency or Delayed Onset 525
19.2 Use of Semg to Characterize Training Exercise 526
19.2.1 Preferential Activation 527
19.2.2 Multi-articular Muscles 528
19.2.3 Characterizing Strength Training Exercises 529
19.2.4 Links Between Coordination and Fatigue in Isometric Task 529
19.2.5 Links Between Coordination and Fatigue in Dynamic Task 530
19.3 Training-Induced Muscle Strength Gain: Neural Factors Versus Hypertrophy 531
19.4 Investigation of Muscle Damage By Means of Surface EMG 536
19.4.1 Acute Effects of Static Stretching on Muscle Soreness 538
19.4.2 Fusimotor Sensitivity After Prolonged Stretch-Shortening Cycle Exercise 540
19.5 Relationships between Emg Features and Muscle Fiber Features 541
References 548
Chapter 20: Surface Electromyography for Man-Machine Interfacing in Rehabilitation Technologies 560
20.1 Introduction 560
20.2 Extraction of Control Signals From the Surface EMG 561
20.2.1 Data-Driven: Machine Learning 562
20.2.2 Model-Driven: EMG-Driven Musculoskeletal Modeling 563
20.3 Function Replacement: Active Prostheses 564
20.4 Function Restoration: Orthotics 568
20.5 Neuromodulation: EMG-Driven Electrical Stimulation and Rehabilitation Robotics 574
20.6 Conclusions 576
Acknowledgment 576
References 576
Index 581
IEEE Press Series in Biomedical Engineering 591
End User License Agreement 593