Search Results (68)

Electromyographic (EMG) signals are increasingly used in wearable cyber–physical systems (CPS), where reliable movement recognition must be achieved under limited computational resources. In this study, we present a compact EMG processing framework that integrates signal acquisition, preprocessing, segmentation, and movement classification within a unified pipeline designed for embedded-oriented applications. The proposed approach combines a multi-channel EMG acquisition system with a lightweight one-dimensional convolutional neural network (1D CNN) developed according to TinyML principles, withprocessing input windows of size 32 × 3 and low computational complexity and memory requirements. Experimental evaluation was conducted on a dataset collected from 15 participants performing squat, walking, and running activities under realistic acquisition conditions. The proposed model achieved an accuracy of 0.9135, an F1-score of 0.9124, and a ROC AUC of approximately 0.96, demonstrating reliable classification performance. Following 8-bit quantization, the model size was reduced to approximately 2 KB, supporting deployment on resource-constrained embedded platforms. The results show that compact CNN architectures can effectively classify EMG-based movement patterns while maintaining a small computational footprint, providing a practical foundation for future wearable CPS and TinyML-enabled applications. Full article

Background/Objectives: Knee osteoarthritis (KOA) is increasingly recognized as a function-limiting condition in which pain, neuromuscular impairment, and reduced physical activity interact with sarcopenic vulnerability to accelerate functional decline. This review reappraises commonly used oral nutraceuticals through a muscle–joint framework and examines whether they can be conservatively positioned as adjuncts that reduce symptom-related barriers to exercise-based care rather than as disease-modifying therapies. Methods: This review was conducted as a structured narrative synthesis informed by SANRA principles, using a structured and transparent search process and dual-independent study selection, without quantitative meta-analysis or formal certainty-of-evidence grading. PubMed/MEDLINE, Embase, and the Cochrane Library were searched for English-language studies published from January 2000 to March 2026, supplemented by reference screening of key reviews and international guidelines. Results: Mechanistic and clinical evidence supports a plausible pathway linking KOA pain, arthrogenic muscle inhibition, reduced loading, physical inactivity, and sarcopenic vulnerability. Across glucosamine/chondroitin, collagen peptides, omega-3 fatty acids, curcumin, and Boswellia, symptomatic benefits were modest, heterogeneous, and formulation-dependent, with no consistent evidence of structural disease modification. Direct evidence that nutraceuticals improve exercise adherence or long-term physical activity remains limited; however, selected exercise-integrated or function-oriented studies show participation-relevant signals in gait speed, activity volume, and performance-based outcomes. Conclusions: Nutraceuticals should be interpreted as optional, time-limited adjuncts within exercise-centered KOA management. Their potential value lies in modest symptom support that may facilitate rehabilitation participation in selected patients, not in stand-alone treatment of KOA or sarcopenia. Full article

Alzheimer’s disease (AD) is a neurodegenerative disorder, whereas myasthenia gravis (MG) is an autoimmune neuromuscular disease. Despite their distinct clinical manifestations, both disorders involve immune dysregulation and cholinergic dysfunction, and epidemiological evidence for an association remains inconclusive. Here, we investigated the genetic architecture underlying the AD–MG relationship using large-scale European-ancestry genome-wide association study (GWAS) data, including early- and late-onset MG, within a multi-resolution analytical framework. Genome-wide analyses indicated modest polygenic overlap between AD and MG, supported by nominally significant and directionally consistent correlations across datasets, SNPeffect concordance in the primary GWAS, and robust gene-level overlap. Evidence for genome-wide correlation was weaker and non-significant across AD-MG subtypes. Local genetic correlation analyses revealed that shared AD-MG signals were largely locus-specific and heterogeneous, with regions showing both concordant and discordant effects, particularly across MG subtypes. Subtype-specific analyses indicated broader and more heterogeneous overlap for AD–late-onset MG, including both major histocompatibility complex (MHC) and non-MHC loci, whereas AD–early-onset MG showed more restricted patterns largely confined to the MHC. Cross-trait meta-analysis and colocalisation further refined these findings, identifying a limited number of loci with evidence of shared AD-MG association, while most regions were consistent with distinct causal variants. A chromosome 16 locus showed the most consistent shared cross-trait AD-MG signal across multiple analytical frameworks. Mendelian randomisation analyses provided no evidence of a causal effect of AD liability on MG and yielded only suggestive, and inconclusive evidence for the reverse direction. Gene-level and expression-informed analyses prioritised immune-related genes, as well as regulators of transcription, chromatin organisation, and synaptic processes, without implying concordant causal variants across traits. Tissue and pathway analyses suggested shared immune involvement, with differential emphasis on innate immune processes in AD and adaptive immune pathways in MG. Notably, heterogeneity of effects within the MHC and across loci suggests that overlap reflects a complex, context-dependent architecture rather than a uniform immune-driven signal. Overall, our findings indicate that the AD–MG relationship is characterised by modest genome-wide polygenic overlap, substantial locus-specific heterogeneity, and partial convergence on immune-related genetic architecture, rather than a uniformly shared mechanism. Full article

The commercial viability of B. purificata relies on efficient depuration. While P. pulchellum extract dramatically accelerates this process, its physiological and molecular mechanisms remain unknown. To address this, we integrated behavioural, histological, ultrastructural, and transcriptomic (RNA-seq) analyses to characterize the acute stress and subsequent recovery phases of B. purificata exposed to the botanical extract. Extract exposure induced severe neuromuscular hyperextension response, with histological analysis revealing acute interstitial oedema and neuronal chromatolysis. This structural damage caused sustained cephalopodium extension and muscle fibre uncoupling. Transcriptomic profiling linked this neuromuscular dysfunction to conserved calcium-dependent adrenergic signalling modules and profound endogenous neuroendocrine disruption. The extract also induced cellular stress, downregulating the apoptosis inhibitor BIRC7 and eliciting transcriptomic signatures consistent with a DNA damage response. Crucially, during the short-term recovery phase, surviving tissues mounted a robust transcriptomic repair response. The snails systemically suppressed the cell cycle via MCM2-6 downregulation while upregulating GADD45 and RAD54B, suggesting a prioritization of genomic repair alongside partial morphological reorganization. Ultimately, P. pulchellum accelerates depuration via acute phytochemical stress and neuromuscular dysfunction. Importantly, this stress is accompanied by a highly coordinated transcriptomic repair response and partial short-term restoration, providing foundational molecular insights essential for evaluating and optimizing botanical depuration protocols. Full article

Parkinson’s disease (PD) is a neurodegenerative disorder affecting millions worldwide, characterized by motor symptoms such as tremor, rigidity, and bradykinesia that significantly impair daily life. The current diagnosis and monitoring rely primarily on clinical observations and rating scales (e.g., the MDS-UPDRS), which are subjective and limited in detecting subtle motor alterations, leading to inter- and intra-rater variability. In recent years, wearable sensors such as surface electromyography (sEMG) and inertial measurement units (IMUs) have emerged as non-invasive tools for quantifying neuromuscular activity and motor performance in PD. When combined with machine learning (ML) and deep learning (DL) techniques, these signals enable the development of models for disease detection, patient classification, and symptom severity assessment. This review provides a structured overview of recent ML and DL approaches applied to surface electromyography for PD monitoring, addressing a gap in the current literature. It analyzes data acquisition strategies, preprocessing techniques, feature extraction methods, model architectures, and evaluation protocols across tasks such as diagnosis, tremor analysis, freezing of gait detection, and gait assessment. Despite promising results, key challenges remain, including limited dataset size, lack of standardization, and poor generalization. Finally, this work highlights emerging trends and identifies a representative processing pipeline to support real-world clinical translation. Full article

Electromyographic (EMG) signals encode complex spatiotemporal dynamics reflecting neuromuscular coordination and pharmacological modulation. This study introduces a unified Hankel–topological framework for reconstructing and analyzing long-duration EMG recordings acquired from pigs under pharmacological influence, and for quantifying their bioelectrical organization. The method couples low-rank Hankel representations—capturing temporal redundancy and smoothness—with topological continuity constraints that stabilize activity packets defined by 5 s silence intervals. Six pigs were recorded across four experimental sessions (24 h each; four channels), and envelope reconstruction was performed using an ADMM-based solver. Quantitative analysis revealed consistent post-drug reductions in the packet rate ($-24.9\%$), the mean duration ($-2.3$ s), the amplitude ($-0.16$ a.u.), the effective Hankel rank ($-3.0$), and topological diversity ($\Delta {\beta }_0=-1.2$; all $p<0.01$). Deeper channels exhibited stronger suppression (interaction $p<0.02$), suggesting depth-dependent neuromuscular effects. The proposed framework unifies dynamical, statistical, and topological perspectives on EMG structure and yields interpretable biomarkers of neuromuscular inhibition and recovery. More broadly, it provides a generalizable signal processing methodology for analyzing structured, noisy physiological time series beyond EMG. Full article

The neuromuscular junction (NMJ) is responsible for transmitting neural signals that trigger muscle contraction. Muscle injuries cause damage to cellular structures and trigger local inflammatory processes. In this context, eccentric contraction was used as an experimental model because it involves excessive stretching, generating mechanical stress. Twenty-five adult male Wistar rats were distributed into groups: Control (C) (n = 5) and Injury (I) (n = 20). The protocol was performed on a treadmill and consisted of 18 sets/5 min/16 m/min speed, with intervals, and with a negative incline (−16º). The analyses consisted of histochemical techniques, such as myofibrillar ATPase and immunofluorescence (calcium channels, synaptophysin and α-bungarotoxin). Group I-0H showed alterations in the presynaptic region and an increase in Type I fibers. I-24H presented disorganization in the postsynaptic region. In I-4D, we observed the reorganization of neuromuscular activity, while I-7D presented greater density and cross-sectional area (CSA) of Type II fibers. It is concluded that the protocol promotes changes in NMJ structure and fiber distribution, mainly in I-24H. In I-4d, a reorganization of neuromuscular activity is observed, and in I-7D, a structural indicator consistent with recovery demonstrates the skeletal muscle’s ability to adapt to injury. Full article

Background: Surface electromyography (sEMG) of periocular muscles is a non-invasive technique used to assess eyelid dynamics and facial neuromuscular function, with applications in ophthalmology, neurology, and rehabilitation. Despite its clinical and research potential, substantial methodological variability—particularly in electrode placement, acquisition parameters, and signal processing—has limited reproducibility and hindered broader clinical translation. A comprehensive synthesis of existing methodologies was therefore needed to support future standardization. Objectives: The review aimed to systematically map current periocular sEMG methodologies, identify sources of methodological heterogeneity, organize findings into structured methodological domains, and develop a conceptual framework along with a minimum reporting set to promote transparency, reproducibility, and comparability across studies. Eligibility Criteria: Studies were eligible if they investigated surface electromyography of periocular muscles and reported methodological details related to electrode placement, signal acquisition, processing, or analysis. Randomized controlled trials, observational studies, and pilot investigations were included. No restrictions were placed on publication year. Sources of Evidence: Comprehensive searches were conducted in PubMed, Embase, and Web of Science from database inception through November 2025. Grey literature sources were also examined to enhance coverage and reduce publication bias. Charting Methods: Two reviewers independently screened records and extracted data. Extracted information was organized into predefined methodological domains. A thematic synthesis approach was used to identify recurring methodological patterns, and findings were integrated into a structured conceptual framework. Results: Sixteen studies published between 2002 and 2025 met the inclusion criteria, encompassing randomized trials, observational studies, and pilot investigations. Considerable heterogeneity was identified across studies in electrode characteristics, placement strategies, reference configurations, sampling frequencies, and normalization procedures. Three recurring methodological domains emerged: instrumentation and acquisition, analytical and normalization approaches, and clinical or experimental applications. Based on these domains, the authors developed a conceptual methodological framework and proposed a minimum reporting set intended to improve methodologyical transparency and support reproducibility and multicenter comparability. Conclusions: Periocular sEMG represents a promising yet methodologically fragmented field. This scoping review provides the first comprehensive synthesis of periocular sEMG practices and establishes an evidence-based platform for standardized acquisition, processing, and reporting. Adoption of the proposed framework may strengthen reproducibility, facilitate multicenter collaboration, and accelerate integration into clinical and research settings. Full article

Background and Objectives: Surface electromyography (EMG) is widely used to assess muscle activation. However, direct interpretation of its functional biomechanical consequences remains challenging. This study aimed to develop and evaluate an EMG-driven musculoskeletal simulation framework for investigating how controlled modifications of muscle activation patterns influence joint-level biomechanics in the upper limb. The objective was not to reproduce specific clinical pathologies but to enable systematic virtual scenario analysis of activation-dependent movement alterations. Materials and Methods: Surface EMG signals were recorded from five healthy adults (3 males, 2 females; age 22 ± 1 years) during cyclic elbow flexion/extension tasks using a wireless system (sampling frequency: 2000 Hz). Processed and normalized EMG envelopes were directly applied as prescribed neural inputs in forward dynamic simulations implemented in OpenSim, without optimization-based muscle recruitment. Controlled virtual scenarios were generated through parametric modification of activation signals to represent reduced activation capacity, increased antagonist co-activation, spasticity-like activation modulation, and tremor-like oscillatory modulation. Joint kinematics, joint moments, and movement stability were evaluated. A Movement Quality Index (MQI) was introduced as a comparative research metric integrating biomechanical performance indicators. Simulations were deterministic and analyzed descriptively. Results: Distinct activation modifications produced characteristic kinematic and kinetic responses. Reduced activation capacity decreased simulated joint moment output, increased co-activation altered joint moment timing and mechanical stability, and tremor-like oscillatory modulation generated periodic fluctuations in joint kinematics and kinetics. The MQI enabled quantitative differentiation between simulated scenarios and severity levels within the controlled modelling framework. Conclusions: The proposed EMG-driven forward dynamic simulation framework provides a methodological platform for controlled virtual scenario analysis of activation-dependent biomechanical changes. The findings highlight the sensitivity of joint-level mechanics to altered muscle activation patterns, within the deterministic modelling environment. The framework is intended for research-oriented biomechanical investigation and hypothesis testing rather than direct clinical diagnosis of neuromuscular disorders. Full article

Background: Duchenne muscular dystrophy (DMD) is a severe neuromuscular disorder caused by the absence of functional dystrophin, leading to progressive muscle degeneration, inflammation, and alterations in the central nervous system. The sustained inflammatory response in DMD increases glial activation and the release of tumor necrosis factor alpha (TNF-α), which contributes to muscle fiber damage. Here, we investigated the anti-inflammatory and neuroprotective effects of Etanercept, a TNF-α receptor-blocking therapeutic, on the spinal cord of MDX mice, a widely used model of DMD. Methods: Adult male MDX and control C57BL/10 mice received vehicle or Etanercept (3, 6, or 12 mg/Kg, intraperitoneally (i.p.)) every 72 h for two weeks, along with daily gait assessment. At the end of treatment, flow cytometry and immunolabeling analyses were performed in the lumbar spinal cord. Results: Etanercept at 12 mg/Kg reduced astrogliosis and microglial activation; restored synaptic markers, including synaptophysin, glutamic acid decarboxylase 65 (GAD-65), and vesicular glutamate transporter 1 (VGLUT-1); and decreased pro-inflammatory cytokines. The treatment reduced GFAP+/TNF-α+ astrocytes and significantly downregulated Th1 lymphocyte polarization in treated MDX mice. These cellular effects were accompanied by improvements in locomotor function. Conclusions: Together, our findings indicate that TNF-α blockade by Etanercept exerts neuroprotective and anti-inflammatory actions in the spinal cord of dystrophic mice, providing new insights into the impact of TNF-α signaling on neuroinflammatory processes in DMD. Full article

Introduction: The biological effects of dietary polyphenols have gained increasing attention due to their roles in regulating oxidative stress, inflammatory processes, and mitochondrial function. Human studies suggest that polyphenol intake may support aspects of post-exercise recovery, neuromuscular function, and selected aspects of physical performance. However, most investigations have been conducted in young or metabolically healthy populations, limiting direct clinical translation to older adults. Objective: This narrative review aims to synthesize current mechanistic and human evidence on the physiological and recovery-related effects of dietary polyphenols in the context of exercise adaptation and skeletal muscle function, and to examine their potential relevance to muscle aging and sarcopenia. Methods: A structured, non-systematic literature search was conducted to integrate findings from human intervention trials, preclinical studies, and mechanistic research addressing polyphenols, exercise adaptation, muscle recovery, and muscle aging. Evidence was synthesized narratively with emphasis on shared physiological pathways and functional outcomes. Results: Human intervention studies suggest that polyphenol intake may attenuate biomarkers of exercise-induced muscle damage, modulate inflammatory responses, and accelerate recovery of muscle strength and functional performance. Mechanistic evidence supports the involvement of redox homeostasis, mitochondrial regulation, and inflammatory signaling as central mediators of these effects. While clinical data in older populations remain limited, converging evidence suggests biological overlap between recovery-related pathways and mechanisms implicated in age-related muscle decline. Conclusions: Current evidence is consistent with a biologically plausible role for polyphenols in modulating exercise-related physiological and recovery processes. By aligning recovery-focused evidence with pathways central to muscle aging, this review proposes a translational framework that may inform the design of future targeted clinical trials in older and clinical populations. Full article

Wearable sensing technologies are increasingly used to assess neuromuscular function during daily-life activities. This study presents and evaluates a multisensor wearable system integrating a textile-based surface Electromyography (sEMG) sleeve and a pressure-sensing insole for monitoring Tibialis Anterior (TA) and Gastrocnemius Lateralis (GL) activation during gait. Eleven healthy adults performed overground walking trials while synchronised sEMG and plantar pressure signals were collected and processed using a dedicated algorithm for detecting activation intervals across gait cycles. All participants completed the walking protocol without discomfort, and the system provided stable recordings suitable for further analysis. The detected activation patterns showed one to four bursts per gait cycle, with consistent TA activity in terminal swing and GL activity in mid- to terminal stance. Additional short bursts were observed in early stance, pre-swing, and mid-stance depending on the pattern. The area under the sEMG envelope and the temporal features of each burst exhibited both inter- and intra-subject variability, consistent with known physiological modulation of gait-related muscle activity. The results demonstrate the feasibility of the proposed multisensor system for characterising muscle activation during walking. Its comfort, signal quality, and ease of integration encourage further applications in clinical gait assessment and remote monitoring. Future work will focus on system optimisation, simplified donning procedures, and validation in larger cohorts and populations with gait impairments. Full article

Electromyography (EMG) has become essential in biomedical engineering, rehabilitation, and human–machine interfacing due to its ability to capture neuromuscular activation for control, monitoring, and diagnosis. Recent advances in sensing hardware, high-density and flexible electrodes, and embedded acquisition modules combined with modern signal processing and machine learning have significantly enhanced the robustness and applicability of EMG-based systems. This review provides an integrated overview of EMG generation, acquisition standards, and preprocessing techniques, including adaptive filtering, wavelet denoising, and empirical mode decomposition. Feature extraction methods across the time, frequency, time–frequency, and nonlinear domains are compared with respect to computational efficiency and suitability for real-time systems. The review synthesizes classical and contemporary pattern-recognition approaches, from statistical classifiers to deep architectures such as CNNs, RNNs, hybrid CNN–RNN models, transformer-based networks, and graph neural networks. Key challenges, including signal non-stationarity, electrode displacement, muscle fatigue, and poor cross-user or cross-session generalization, are examined alongside emerging strategies such as transfer learning, domain adaptation, and multimodal fusion with IMU or FMG signals. Finally, the paper surveys rapidly growing EMG applications in prosthetics, rehabilitation robotics, human–machine interfaces, clinical diagnostics, and sports analytics. The review highlights ongoing limitations and outlines future pathways toward robust, adaptive, and deployable EMG-driven intelligent systems. Full article

Background: This study aimed to explore the effects of neuromuscular electrical stimulation (NMES) combined with water-based resistance training on muscle activation and coordination during freestyle kicking. Methods: Thirty National Level male freestyle swimmers were randomly assigned to an experimental group (NMES + water-based training) or a control group (water-based training only) for a 12-week intervention. The experimental group received NMES pretreatment before each session. Underwater surface electromyography (sEMG) synchronized with high-speed video was used to collect muscle activation data and corresponding kinematic information during the freestyle kick. The sEMG signals were then processed using time-domain analysis, including integrated electromyography (iEMG), which reflects the cumulative electrical activity of muscles, and root mean square amplitude (RMS), which indicates the intensity of muscle activation. Non-negative matrix factorization (NMF) was further applied to extract and characterize muscle synergy patterns. Results: The experimental group showed significantly higher iEMG and RMS values in key muscles during both kicking phases. Within the core propulsion synergy, muscle weighting of vastus medialis and biceps femoris increased significantly, while activation duration of the postural adjustment synergy was shortened. The number of synergies showed no significant difference. Conclusions: NMES combined with water-based resistance training enhances muscle activation and optimizes neuromuscular coordination strategies, offering a novel approach to improving sport-specific performance. Full article

Objective: to clarify differences in the intermuscular coherence of core muscles during side kicks among male Sanda athletes at varying skill levels, particularly in critical frequency bands; to reveal the association between neuromuscular coordination mechanisms and technical proficiency; and to provide methodological references for quantitative analysis of combat sports techniques. Methods: Thirty-six male Sanda athletes were divided into professional (n = 18) and amateur (n = 18) groups based on athletic ranking and training duration. Surface electromyographic (EMG) signals from 15 core muscles and kinematic data were synchronously recorded using a wireless EMG system and a high-speed camera. Signal processing extracted root mean square amplitude (RMS) and integral EMG (iEMG). Muscle coordination was quantified via time-frequency coherence analysis across alpha (8–15 Hz), beta (15–30 Hz), and gamma (30–50 Hz) bands. Results: The professional group exhibited significantly higher RMS and iEMG values in most core muscles (e.g., rectus femoris RMS: 0.298 ± 0.072 vs. 0.214 ± 0.077 mV, p < 0.001). Regarding intermuscular coherence, the professional group demonstrated significantly superior coherence in the α, β, and γ bands for key muscle pairs, including upper limb–swing leg, support leg–swing leg, and upper limb–support leg. Notable differences were observed in pairs such as external oblique–rectus femoris (alpha band: 0.039 ± 0.012 vs. 0.032 ± 0.011, p < 0.01) and right rectus femoris–biceps femoris (beta band: 0.033 ± 0.010 vs. 0.023 ± 0.007, p < 0.01). Conclusions: The fundamental difference in side kick technique among Sanda athletes lies in neuromuscular control strategies and muscle coordination efficiency. Sensor-based intermuscular coherence analysis provides an objective quantitative indicator for distinguishing technical proficiency, offering a scientific basis for optimizing training and extending the methodological framework for technique assessment in combat sports. Full article