Mechanomyography: An Insight to Muscle Physiology

Talib, Irsa; Sundaraj, Kenneth; Lam, Chee Kiang; Ali, Md. Asraf; Hussain, Jawad

doi:10.1007/978-981-13-9539-0_13

Cited by 9 publications

(7 citation statements)

References 24 publications

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“…Literature indicates that CA is a measure of muscle and limb size [20]. This observation is also supported by the important role of all the three muscles observed in this study during elbow joint exion task [21]. Similarly, most of signi cant Pearson's correlations between MMG RMS and muscle torque were found during exion task.…”

Section: Discussionsupporting

confidence: 87%

Analysis of Anthropometrics and Mechanomyography Signals as Forearm Flexion, Pronation and Supination Torque Predictors

Talib¹,

Sundaraj

Lam

et al. 2022

Preprint

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The aim of this study was to analyze anthropometrics and mechanomyography (MMG) signals as forearm flexion, pronation and supination torque predictors. Twenty-five young, healthy, male participants performed the isometric forearm flexion, pronation and supination tasks from 20 to 100% maximal voluntary contraction (MVC) maintaining right angle at elbow joint. Nine anthropometric measures and MMG signals from biceps brachii (BB), brachialis (BRA) and brachioradialis (BRD) muscles recorded using triaxial accelerometers were correlated with torque values. Significant positive correlations were found for arm circumference (CA) and MMG root mean square (RMS) values with flexion torque at five submaximal to maximal MVC levels. Flexion torque might be predicted using arm circumference (r = 0.426–0.557), a pseudo for muscle size and MMG RMS (r = 0.404–0.470), an indication of muscle activation.

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Section: Discussionsupporting

confidence: 87%

Analysis of Anthropometrics and Mechanomyography Signals as Forearm Flexion, Pronation and Supination Torque Predictors

Talib¹,

Sundaraj

Lam

et al. 2022

Preprint

Self Cite

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“…Mechanomyography (MMG) is also suitable for assessing skeletal muscle activities and monitoring fatigue and force imparted in muscle movement [54,55]. In the literature, the measurement of MMG typically employs IMU [55], piezoelectric [56], microphones, pressure sensors, laser distance sensor, and others [27]. Multimodal MMG solutions have been studied, as in [56]; The authors combined forearm acceleration and piezoelectric MMG sensors in the biceps brachii, brachioradialis, and pectoralis major to study mechanical muscle oscillations in patients with Parkinson's condition.…”

Section: Mechanomyography (Mmg)mentioning

confidence: 99%

“…On the other hand, mechanomyography (MMG) measures the mechanical signals resulting from the lateral movement of muscle fibers at low-frequency [25,26]. MMG does not need an electrical connection with the skin and can be detected by various types of sensors, such as IMU, piezoelectric, microphones, pressure sensors, laser sensors, and many others [27]. In [2], facial muscle acoustic mechanomyography (AMMG) has been proposed.…”

Section: Introductionmentioning

confidence: 99%

InMyFace: Inertial and Mechanomyography-Based Sensor Fusion for Wearable Facial Activity Recognition

Bello¹,

Marin²,

Suh³

et al. 2023

Preprint

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Recognizing facial activity is a well-understood (but non-trivial) computer vision problem. However, reliable solutions require a camera with a good view of the face, which is often unavailable in wearable settings. Furthermore, in wearable applications, where systems accompany users throughout their daily activities, a permanently running camera can be problematic for privacy (and legal) reasons. This work presents an alternative solution based on the fusion of wearable inertial sensors, planar pressure sensors, and acoustic mechanomyography (muscle sounds). The sensors were placed unobtrusively in a sports cap to monitor facial muscle activities related to facial expressions. We present our integrated wearable sensor system, describe data fusion and analysis methods, and evaluate the system in an experiment with thirteen subjects from different cultural backgrounds (eight countries) and both sexes (six women and seven men). In a one-model-per-user scheme and using a late fusion approach, the system yielded an average F1 score of 85.00% for the case where all sensing modalities are combined. With a cross-user validation and a one-model-for-all-user scheme, an F1 score of 79.00% was obtained for thirteen participants (six females and seven males). Moreover, in a hybrid fusion (cross-user) approach and six classes, an average F1 score of 82.00% was obtained for eight users. The results are competitive with state-of-the-art non-camera-based solutions for a cross-user study. In addition, our unique set of participants demonstrates the inclusiveness and generalizability of the approach.

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“…Numerous investigations have shown how properties derived from vibration can be used to evaluate muscle activity and muscular performance [19]. MMG has been used in clinical applications to detect and monitor muscular pain [41], diagnose muscle hypertrophy and atrophy [56], and for the examination of neuromuscular disorders [57]. A better understanding of the composition and characteristics of the MMG signal could strengthen the usability of the myographic signal.…”

Section: Functional Muscle Networkmentioning

confidence: 99%

3D muscle networks based on vibrational mechanomyography

Mancero Castillo,

Atashzar,

Vaidyanathan

2023

J. Neural Eng.

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Objective: Muscle network modeling maps synergistic control during complex motor tasks. Intermuscular coherence (IMC) is key to isolate synchronization underlying coupling in such neuromuscular control. Model inputs, however, rely on electromyography (EMG), which can limit the depth of muscle and spatial information acquisition across muscle fibers.Approach: We introduce three-dimensional muscle networks based on vibrational mechanomyography (vMMG) and IMC analysis to evaluate the functional co-modulation of muscles across frequency bands in concert with the longitudinal, lateral, and transverse directions of muscle fibers. vMMG is collected from twenty subjects using a bespoke armband of accelerometers while participants perform four hand gestures. IMC from four superficial muscles (flexor carpi radialis, brachioradialis, extensor digitorum communis, and flexor carpi ulnaris) is decomposed using matrix factorization into three frequency bands.We further evaluate the practical utility of the proposed technique by analyzing the network responses to various sensor-skin contact force levels, studying changes in quality, and discriminative power of vMMG.Main Results: Results show distinct topological differences, with coherent coupling as high as 57% between specific muscle pairs, depending on the frequency band, gesture, and direction.No statistical decrease in signal strength was observed with higher contact force.Significance: Results support the usability vMMG as a tool for muscle connectivity analyses and demonstrate the use of IMC as a new feature space for hand gesture classification.Comparison of spectrotemporal and muscle network properties between levels of force support the robustness of vMMG-based network models to variations in tissue compression. We argue three-dimensional models of vMMG-based muscle networks provide a new foundation for studying synergistic muscle activation, particularly in out-of-clinic scenarios where electrical recording is impractical.

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Mechanomyography: An Insight to Muscle Physiology

Cited by 9 publications

References 24 publications

Analysis of Anthropometrics and Mechanomyography Signals as Forearm Flexion, Pronation and Supination Torque Predictors

Analysis of Anthropometrics and Mechanomyography Signals as Forearm Flexion, Pronation and Supination Torque Predictors

InMyFace: Inertial and Mechanomyography-Based Sensor Fusion for Wearable Facial Activity Recognition

3D muscle networks based on vibrational mechanomyography

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