Synergistic Upper-Limb Functional Muscle Connectivity Using Acoustic Mechanomyography

Castillo, C. Sebastian Mancero; Vaidyanathan, Ravi; Atashzar, S. Farokh

doi:10.1109/tbme.2022.3150422

Cited by 4 publications

(6 citation statements)

References 63 publications

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“…IMC based on MMG is a new area of study initially proposed by the authors. In [21] we found coupling strengths as high as 0.55 by the use of acoustic MMG. In line with these previous findings, results here show similar values of coupling strengths for vMMG.…”

Section: Muscle-pairs Coherencementioning

confidence: 71%

“…Information was recorded while participants performed different hand gesture tasks. The experimental protocol followed the one described in [21]. For this study, gestures and target muscles were maintained.…”

Section: Experimental Designmentioning

confidence: 99%

“…The higher the value of IMC, the more coupling of the muscles in the frequency domain. IMC was extracted between each muscle pair following the calculation described in [21] for the computation of modified magnitude squared coherence. In order to consider the entire spectral space of the samples and to avoid the presence of spurious coherence caused by edge effects, each 3 s trial of data was zero-padded at both ends by a number of samples equal to half the samples needed to account for 1024 segments for the calculation of coherence.…”

Section: Intermuscular Coherencementioning

confidence: 99%

“…In this way, NNMF was applied to all coherence matrices c x m corresponding to each muscle pair, gesture, and direction. A multiplicative update rule was used to minimize the objective function and constraint the scope of each component to that of the frequency bands, as described in [21]. IMC of all muscle pairs was decomposed over the intervals specified above into non-negative factors using an alternating least squares algorithm, thus resulting in two non-negative matrices reflecting the spectral signatures (basis vectors) and the corresponding coupling strengths between all muscle pairs for each gesture and each direction.…”

Section: Spectral Decompositionmentioning

confidence: 99%

“…out of laboratory/clinic). Muscle network analysis by the use of acoustic MMG (aMMG) was previously introduced by the authors in [21] for the evaluation of functional intermuscular connectivity alterations linked to upper-limb loss and differences in muscle network topologies derived from different hand gesture tasks. The results showed evidence that the spectral features of the aMMG signal can be used to construct muscle connectivity network modelS reflecting the common spectral patterns shared by upper-limb muscle groups when performing different motor tasks.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Muscle-pairs Coherencementioning

confidence: 71%

Section: Experimental Designmentioning

confidence: 99%

Section: Intermuscular Coherencementioning

confidence: 99%

Section: Spectral Decompositionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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3D muscle networks based on vibrational mechanomyography

Mancero Castillo,

Atashzar,

Vaidyanathan

2023

J. Neural Eng.

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Coherence Networks of Bilateral Upper Limb Motions in Chronic Stroke Patients

Pan

Liu

2023

J. Mech. Med. Biol.

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Understanding the mechanisms of how the skeletal system reduces multiple degrees of freedom in motor control is very important in the field of neurorehabilitation. We therefore need to understand the functional connectivity of these synergistic patterns in bilateral upper limb of stroke patients from different Brunnstrom Stages. The intermuscular coherence at different frequency bands is decomposed to obtain muscle synchronization. Muscle networks were then applied to describe the functional connectivity between muscle pairs. We recruited 35 stroke patients with Brunnstrom Stages III-VI. Muscle activity was acquired from seven muscles around forearm while participants performed voluntary upward reaching with both affected and unaffected sides. We decomposed IMC spectra by negative matrix factorization to identify shared frequencies and co-modulated muscles. Two muscle networks were found and functional muscle networks are evaluated by strength, transitivity and global efficiency. Results showed that the network topology was different significantly between the two sides and between Brunnstrom stages of stroke patients. These findings can throw light on the understanding of motor control and rehabilitation of motor impairment for stroke survivors, which may promote the post-stroke rehabilitation process by detecting impaired neuromuscular coordination in frequency domain.

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Utilization of Near Infrared Spectroscopy Imaging at Nanoscale for the Identification of Biomarkers in Sports-Induced Muscle Fatigue

Wu,

Yang

2023

Journal of Nanoelectronics and Optoelectronics

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This study integrates Near Infrared Spectroscopy (NIRS) and nanoscale imaging technologies to discern alterations in muscle tissue biomarkers, thereby enhancing the precision of non-invasive monitoring of muscle fatigue. Experimental investigations were carried out on the biceps brachii muscle of 12 subjects, categorized into mild, moderate, and severe fatigue groups. Concurrently, a specific wavelength of Near Infrared Laser Diode (NIR-LD) was employed to acquire spectral data. The application of Atomic Force Microscopy (AFM) in conjunction with NIRS imaging facilitated the attainment of high-resolution images of the biceps brachii tissue. The absorption characteristics of distinct biomarkers in muscle tissue, responsive to near-infrared light, were captured to calculate concentration variations and evaluate muscle fatigue levels. The findings revealed substantial variations in the concentrations of Oxy-hemoglobin (HbO), Deoxy-hemoglobin (HbR), Lactic Acid (LA), Phosphocreatine (PCr), Troponin (Tn), Creatine Kinase (CK), and Glutamine (Gln) across different fatigue groups. Muscle fatigue assessment exhibited an average sensitivity, accuracy, specificity, and F1 score of 0.96, 0.96, 0.95, and 0.96, respectively, for the 12 subjects. The average Area Under the Curve (AUC) values for detecting mild, moderate, and severe fatigue were 0.96, 0.96, and 0.98, respectively. This method demonstrates notable accuracy in the identification of muscle fatigue biomarkers, rendering it suitable for sports-related muscle fatigue assessment.

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Synergistic Upper-Limb Functional Muscle Connectivity Using Acoustic Mechanomyography

Cited by 4 publications

References 63 publications

3D muscle networks based on vibrational mechanomyography

3D muscle networks based on vibrational mechanomyography

Coherence Networks of Bilateral Upper Limb Motions in Chronic Stroke Patients

Utilization of Near Infrared Spectroscopy Imaging at Nanoscale for the Identification of Biomarkers in Sports-Induced Muscle Fatigue

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