Topic
Mechanomyogram
About: Mechanomyogram is a research topic. Over the lifetime, 237 publications have been published within this topic receiving 7822 citations. The topic is also known as: MMG.
Papers published on a yearly basis
Papers
28 Jan 2005
TL;DR: This work focuses on the development of models for Surface EMG Signal Generation based on the principles of Structure--Based SEMG models, which were developed in the context of motor control and Muscle Contraction.
Abstract: 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. Sogaard). 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. Hagg, 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.
1,191 citations
Journal Article•
TL;DR: The results indicate that the muscular sound may be an adjunct tool to the force, the physiological force tremor, and electromyogram to obtain information on the muscle mechanical model as well as on muscle motor control.
Abstract: Muscular sound is a mechanical phenomenon detectable at the surface of an active muscle, which has been known and described since 1800. Only recently, because of the availability of reliable transducers and sophisticated analysis techniques, has this signal become attractive for monitoring the mechanical aspects of muscle contraction. The muscular sound characteristics were investigated both during electrically elicited and voluntary contractions. In the first case, the influence of the biophysical and mechanical properties of the muscle on this signal was studied. During voluntary efforts the summation of the mechanical activity of the recruited motor units was analyzed. The results indicate that the muscular sound may be an adjunct tool to the force, the physiological force tremor, and electromyogram to obtain information on the muscle mechanical model as well as on muscle motor control. This review focuses on the following aspects of the signal: (1) recording problems; (2) muscle sound properties during stimulation of isolated and in vivo muscle; (3) time and frequency domain analysis during non fatiguing and fatiguing contractions; (4) comparison with other signals related to muscle activity; (5) discussion on the origin; and (6) possible practical applications.
550 citations
TL;DR: The alteration in the MMG and EMG parameters vs. %MVC relationships at fatigue seems to be related to the impossibility of recruiting fast, but more fatigable MUs, and to the lowering of the global MUs firing during the short isometric force ramp investigated.
Abstract: The surface mechanomyogram (MMG) (detectable at the muscle surface as MMG by accelerometers, piezoelectric contact sensors or other transducers) is the summation of the activity of single motor units (MUs). Each MU contribution is related to the pressure waves generated by the active muscle fibres. The first part of this article will review briefly the results obtained by our group studying the possible role of motor unit recruitment and firing rate in determining the characteristics of the MMG during stimulated and voluntary contractions. The second part of this article will study the MMG and EMG during a short isometric force ramp from 0 to 90% of the maximal voluntary contraction (MVC) in fresh and fatigued biceps brachii. The aim is to verify whether changes in motor unit activation strategy in voluntarily fatigued muscle could be specifically reflected in the time and frequency domain parameters of the MMG. MMG-RMS vs. %MVC: at fatigue the MMG-RMS did not present the well known increment, when effort level increases, followed by a clear decrement at near-maximal contraction levels. MMG-MF vs. %MVC: compared to fresh muscle the fatigued biceps brachii showed an MF trend significantly shifted towards lower values and the steeper MF increment, from 65 to 85% MVC, was not present. The alteration in the MMG and EMG parameters vs. %MVC relationships at fatigue seems to be related to the impossibility of recruiting fast, but more fatigable MUs, and to the lowering of the global MUs firing during the short isometric force ramp investigated.
264 citations
TL;DR: The acoustic signal may be useful as a noninvasive monitor of muscle resonant frequency during contraction and the resonant frequencies determined during the force plateau and during relaxation spanned the same range as the peak instantaneous frequency of the acoustic signal.
Abstract: The changes in mechanical resonant frequency of whole muscles during twitch and tetanic contractions are compared to changes in frequency components of the pressure wave produced by muscles during contraction. Resonant frequencies were determined by imposing sinusoidal length changes on a muscle and observing transverse standing waves when the frequency of length change matched the muscle's resonant frequency or a harmonic of the resonant frequency. Acoustic signal instantaneous frequency spectra were calculated using time-frequency transformations including the Wigner transform and the exponential distribution. During a tetanic muscle contraction, the peak instantaneous frequency initially increased and then became constant as the force plateau was reached. The resonant frequencies determined during the force plateau and during relaxation spanned the same range as the peak instantaneous frequency of the acoustic signal. These results suggest that the acoustic signal may be useful as a noninvasive monitor of muscle resonant frequency during contraction. >
206 citations
TL;DR: The data indicate that the MMG derives from the summation of the active muscle fibres twitches and that the latter is linear only for very low firing rates.
183 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 2021 | 2 |
| 2020 | 3 |
| 2019 | 4 |
| 2018 | 8 |
| 2017 | 10 |
| 2016 | 12 |