Search Results (1,211)

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

Upper extremity (UE) pain and pathology are prevalent among manual wheelchair users (MWUs) due to repetitive loading demands, highlighting the need for tools to identify high-risk tasks and inform injury prevention. This study investigated the feasibility of classifying activity intensity for wheelchair-related tasks using wearable sensors and supervised machine learning. Twenty-four MWUs with chronic spinal cord injury completed a standardized mobility course and simulated activities of daily living while UE electromyography (EMG) and inertial measurement unit (IMU) data were collected. Signals segmented into 3, 5, and 10 s windows, and time- and frequency-domain features were extracted and labeled as low, moderate, or high intensity. Multiple classification algorithms were evaluated using subject-dependent and subject-independent cross-validation, and dimensionality reduction was explored to assess class separability. Subject-dependent analyses demonstrated performance above chance but below 75% accuracy, with decision tree models demonstrating superior performance, particularly when trained on data segmented into 5 s windows. IMU features outperformed EMG features, but combining signal types enhanced performance. Subject-independent analyses revealed similar overall accuracy across signal types, but decreased high-intensity classification for EMG data, indicating subject dependency. Findings support the potential of wearable sensor-based machine learning with population-specific findings for activity intensity classification in MWUs, while highlighting challenges related to inter-subject variability for injury risk prediction. Full article

Surface electromyography (sEMG) provides a non-invasive measure of the neural drive transmitted from the central nervous system to muscles by capturing the spatiotemporal summation of motor unit action potentials at the skin surface, and is therefore widely used to study neuromuscular coordination during motor tasks. By reflecting neural drive transmitted from the central nervous system to peripheral muscles, sEMG provides valuable insights for investigating neuromuscular coordination during upper-limb motor tasks. Within the framework of modular motor control, muscle synergy analysis has been increasingly applied to characterize coordinated muscle activation patterns extracted from multi-channel sEMG recordings. In this study, sEMG signals were collected from twelve stroke patients and nine healthy subjects during robot-assisted upper-limb training, involving two movement trajectories (straight and rectangular) and multiple robot-assisted levels. Muscle synergies were extracted using non-negative matrix factorization (NMF). A synergy merging–splitting model, combined with a Functional Driving Ratio (FDR), was employed to characterize both the muscle synergy reorganization and the relative activation contributions of driving versus stabilizing muscle components in terms of motor control strategy. The results showed that healthy subjects maintained consistent muscle coordination patterns across different assistive levels, while making task-dependent adjustments to muscle activation to adapt to variations in movement trajectories. For stroke patients, higher functional status was correlated with more differentiated coordination patterns and relatively higher FDR values, suggesting greater reliance on task-relevant agonist muscles during movement execution. In contrast, lower-function patients exhibited less differentiated coordination patterns accompanied by reduced FDR values, indicating the increased involvement of stabilizing or antagonist muscles. This shift may reflect compensatory control strategies and the reduced efficiency of neuromuscular coordination during assisted upper-limb movements. These findings suggest that sEMG-based muscle synergy features and the FDR may provide quantitative, sensor-derived support for characterizing neuromuscular coordination during robot-assisted rehabilitation. Full article

Multimodal epileptic seizure detection using physiological biosignals remains challenging due to signal noise, inter-subject variability, weak cross-modal alignment, and the limited interpretability of many machine learning models. To address these challenges, this study proposes a parameterized multimodal feature-fusion framework that unifies normalization, modality weighting, and nonlinear cross-modal interaction within a single mathematical representation. Four fusion parameters, the fusion exponent $\rho$, interaction weight $\left(\delta \right)$, normalization factor $\left(\lambda \right),$ and the cross-modal interaction term $\left(\eta \right)$, are introduced at the feature-fusion level, while all classifiers retain their original learning mechanisms. The framework is evaluated using synchronized EEG, ECG, EMG, and accelerometer signals from 120 subjects, segmented into 2 s windows at 512 Hz and analyzed using twelve classical and deep learning classifiers. Principal Component Analysis (PCA) applied to the fused feature space reveals improved class separability compared to unimodal representations, with EEG exhibiting the strongest intrinsic discrimination and peripheral modalities contributing complementary structure when fused. SHapley Additive exPlanations (SHAP) further identify entropy as the most influential feature across all modalities, followed by RMS and energy, yielding physiologically coherent attributions. Quantitative performance evaluation and ablation analysis confirm that the observed improvements arise from the proposed representation design rather than classifier-specific modifications. Unlike existing architecture-dependent fusion strategies, the proposed method introduces a mathematically parameterized feature-space formulation that enhances separability and interpretability without modifying classifier architectures, thereby establishing a representation-driven paradigm for explainable multimodal seizure detection. These results demonstrate that mathematically principled feature-space modeling can simultaneously enhance predictive performance and interpretability, providing a transparent and robust foundation for explainable multimodal seizure detection. 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

The aims of this research were to characterize neuromuscular control features within the gait cycle in Parkinson’s disease (PD) from the perspectives of muscle synergies and cross-modal coupling and to propose a joint representation of the relationship between muscle activation patterns and kinematic dynamic outputs. PD participants (n = 19) were included. Lower-limb surface electromyography (EMG) and kinematic dynamic channels, including pelvic/hip, knee, and ankle angular acceleration, were collected during level-ground natural walking. EMG signals were first decomposed using non-negative matrix factorization (NMF) to extract muscle synergies, and the number of synergies was evaluated using reconstruction performance (R²). Multimodal matrix factorization (MMF) was then applied to jointly decompose the EMG and angular-acceleration channels, yielding a cross-modal synergy representation comprising a shared temporal structure (H) and modality-specific weight structures (W): non-negativity was imposed on EMG weights, whereas kinematic weights were allowed to take positive and negative values to encode directional contributions. Under the current task and muscle set, NMF achieved high EMG reconstruction performance with four synergies (R² = 0.882). The synergy weights showed an ankle-dominant pattern: tibialis anterior (TA) consistently carried high weights across multiple synergies, while lateral gastrocnemius (LG) and soleus (SOL) contributed prominently to another synergy. The synergy activation profiles exhibited phase-dependent fluctuations with multiple rises and falls across the gait cycle, suggesting that synergy output was primarily characterized by continuous modulation rather than single-peak recruitment. MMF further identified eight cross-modal synergies, simultaneously capturing the shared contributions of key muscle groups (e.g., RF, TA, and SOL) and pelvic/hip and knee/ankle angular-acceleration channels within the same decomposition framework and summarizing their descriptive co-variation through the shared temporal structure (H). Overall, A low-dimensional synergy analysis combining EMG-only NMF with cross-modal MMF enables simultaneous characterization of cohort-level modular organization of muscle activity during gait and its descriptive association with pelvis-to-lower-limb dynamic output. This joint framework provides a methodological basis for quantitatively describing gait-related modular organization and temporal modulation patterns in this PD cohort under natural level-ground walking and lays the groundwork for subsequent testing of associations between synergy features and gait phenotypes, clinical severity, and rehabilitation responses. Full article

Background/Objectives: Intraoperative neuromonitoring (IONM) has improved safety in thyroid and parathyroid surgery, yet intermittent IONM (I-IONM) may miss traction injuries developing between stimulations. We evaluated the feasibility and clinical utility of a trend-based intermittent monitoring mode (NIM Vital NerveTrend®) that records closely spaced stimulations and plots amplitude and latency over time. Methods: We conducted a prospective observational study at a high-volume endocrine surgery unit (January–September 2025). Forty-four consecutive patients undergoing thyroidectomy and/or parathyroidectomy with NerveTrend® were enrolled. Electromyography (EMG) responses were categorized as Green (amplitude > 50% of baseline and latency < 110%), Yellow (amplitude < 50% or latency > 110%), Red (amplitude < 50% and latency > 110%), and Loss of Signal (LOS: amplitude <100 µV). Primary outcomes included LOS prevalence and the association between stimulation frequency and the appearance of Yellow trends. Ethical approval: AVEN protocol 486/2024/OSS/AOUPR; informed consent obtained. Results: Of 71 nerves at risk (NAR), 55 had a valid baseline and were analyzed; LOS occurred in 3/55 NAR (5.5%). The mean number of stimulations per NAR was 4.5 (range 1–9). Cases with both Green and Yellow points had a significantly higher mean number of stimulations than cases with only Green points (5.1 vs. 3.8; Student’s t-test p = 0.0059). One Red measurement occurred in a case that progressed to LOS. Conclusions: NerveTrend® provided near real-time functional feedback while maintaining the simplicity of I-IONM. Increased stimulation frequency was associated with early Yellow trend alerts, potentially signaling traction stress and enabling timely surgical adjustments. Larger multicenter studies and protocol standardization are warranted. Full article

Background: Lumbar spondylosis is a frequent cause of chronic low back pain, often associated with radiculopathy. Although imaging evaluation is widely used, it does not always reflect the degree of functional impairment of the nerve roots. Electrophysiological assessments, such as nerve conduction studies (NCS) and surface electromyography (sEMG), can provide additional information on neuromuscular function under conservative treatment. Methods: This quasi-experimental study included 60 patients with lumbar spondylosis and 25 healthy subjects, who underwent clinical, imaging, and electrophysiological assessments. NCS and sEMG parameters were assessed in the patient group before and six months after rehabilitation treatment. The control group was assessed only once, at baseline. We analyzed the nerve conduction velocity of the tibial and peroneal nerves and the sEMG activity of the tibialis anterior muscle bilaterally. Statistical analysis used nonparametric tests, Spearman’s coefficient, and Hodges–Lehmann estimates. Results: Compared to the control group, patients presented increased residual latencies and reduced CMAP amplitude and motor conduction velocity values (p < 0.001). After rehabilitation treatment, significant improvements in NCS parameters were observed, with decreased latencies and increased CMAP amplitude and motor conduction velocity bilaterally (p < 0.001). Also, sEMG amplitude and recruitment pattern scores increased significantly at the 6-month follow-up (p ≤ 0.004). Correlations between electrophysiological parameters and the severity of imaging changes were limited, with modest associations for left tibial latencies (ρ = 0.401–0.467; p < 0.050). Conclusions: In patients with lumbar spondylosis, rehabilitation treatment was associated with functional improvements in nerve conduction velocity parameters and muscle activity. Full article

Background/Objectives: Diabetic neuropathy (DN), a common complication of type 2 diabetes mellitus, manifests as peripheral nerve dysfunction with symptoms such as fatigue. Although exercise effectively reduces fatigue in neuropathy patients, precise detection methods are crucial to elucidate the role of rehabilitation. Accordingly, this study aimed to evaluate fatigue in DN patients using a multimodal approach (clinical and instrumental) and to compare the efficacy of aerobic versus resistance training on fatigue parameters. Methods: Eligible DN inpatients admitted for rehabilitation at the Neuromotor Rehabilitation Unit of the IRCCS ICS Maugeri Institute of Montescano (PV) were enrolled. Inclusion criteria included age between 65 and 85 years and confirmation via the Michigan Neuropathy Screening Instrument (anamnestic section: ≥7; clinical section: ≥2.5). Patients with confounding orthopedic, neurologic, or unstable cardiopulmonary/diabetic conditions were excluded. Overall, 36 participants were randomized into two groups: 17 underwent aerobic training (treadmill), while 19 received resistance training (elastic bands), both as supplements to a standard rehabilitation program. Assessments at baseline and post-training comprised clinical measures (Borg CR10 scale, Functional Independence Measure (FIM) total and subitems, Six-Minute Walk Test (6MWT), fasting blood glucose) and instrumental evaluations (sEMG of the tibialis anterior muscle to analyze conduction velocity intercept, slope, and changes). Results: All patients completed the protocol without dropout or adverse events. Both groups demonstrated significant improvements in FIM scores and post-exercise perceived exertion over time. Instrumental sEMG analysis confirmed a physiological fatigue trend manifested as conduction velocity reduction, yet revealed no significant differences between groups. Conclusions: Multimodal assessment provides an effective means to characterize fatigue in DN patients. Both aerobic and resistance modalities enhance functional independence and fatigue perception. Its early identification enables clinicians to tailor rehabilitation strategies to overcome exercise barriers. Full article

Time–frequency (TF) characterization of electromyographic (EMG) bursts is essential for accurately assessing muscle function, particularly when the signals exhibit a high degree of nonstationarity. In this exploratory study, we investigated the temporal dynamics of the spectral components associated with short-latency EMG bursts using several TF analysis techniques. Specifically, we compared the performance and interpretability of spectrograms obtained via the short-time Fourier transform (STFT), the continuous wavelet transform (CWT), and noise-assisted multivariate empirical mode decomposition (NA-MEMD), applied to EMG signals recorded from the biceps femoris muscle of freely moving rats in an animal model of Parkinson’s disease, acquired using chronically implanted bipolar electrodes during treadmill locomotion. For each method, we evaluated its effectiveness in capturing transient variations in frequency content, the stability of extracted features across bursts, and the extent to which these features reflect physiologically meaningful aspects of muscle activation. The results show that TF approaches reveal complementary information about burst structure; NA-MEMD provides greater adaptability to nonlinear and nonstationary components, whereas STFT- and CWT-based representations offer more controlled and comparable analyses. Overall, these findings highlight the value of TF analysis as a methodological tool for evaluating muscle function and provide a solid foundation for selecting analytical strategies in studies where EMG bursts exhibit complex and highly variable spectral profiles. Full article

Background/Objectives: Visual motion is a powerful contributor to postural control, yet its influence on modulation of the Ia afferent pathway remains to be confirmed. This study investigated whether optic-flow simulating self-motion modulates the soleus Hoffmann (H) reflex recorded in the soleus during upright stance in immersive virtual reality. Methods: Fourteen healthy adults completed two experimental sessions, each comprising four visual conditions of increasing optic-flow complexity. In one session, participants stood freely on a force platform (free standing) whereas in the other, postural sways were restricted (supported standing). Surface EMG, posterior tibial nerve stimulation, and force-platform recordings were collected. Results: During free standing, optic flow substantially increased postural sway [F(3,13) = 15.7, p < 0.001, η² = 0.55], with higher sway in all optic-flow conditions (~13 mm/s) compared with static viewing (~10 mm/s). In contrast, soleus H-reflex amplitude was not modulated by optic flow [F(3,13) = 0.2, p = 0.57], remaining stable across conditions (~44% M_max). Background EMG and CoP position preceding stimulation were similar across conditions. In supported standing, used to isolate the effect of optic flow independently to postural control, H-reflex amplitude again showed no condition effect [F(3,13) = 0.2, p = 0.86]. Conclusions: These findings indicate that postural perturbation induced by optic flow was not accompanied by a modulation of the Ia afferent-motoneuron transmission of the soleus under the used experimental conditions. The results suggest that postural control under virtual optic flow is mediated predominantly by supraspinal sensory-integration mechanisms, rather than by modulation of the Ia-monosynaptic reflex pathway. Full article

Human physiology is among the most complex systems in nature, characterized by intricate structural and functional networks and rich temporal dynamics. Electrophysiological signals produced by different tissues/organs reflect physiological activity, and are inherently non-stationary, non-linear, and noisy. This work focuses on fractal analysis, a framework that captures the self-similar and scale-free properties of electrophysiological signals, which is considered to act as an output of complex physiological structures that generate complex processes. Central to this approach is the principle of ‘decomplexification’, whereby aging and disease are associated with a loss of physiological complexity. We discuss key algorithms, particularly Higuchi’s fractal dimension, which is often combined with other nonlinear measures and machine-learning models for real-time analysis of electrophysiological signals. Evidence shows that fractal metrics enable the early detection and monitoring of neurological and psychiatric disorders, outperforming traditional spectral measures. In movement disorders and mood disorders, fractal and nonlinear features show high diagnostic accuracy. Beyond diagnostics, we discuss therapeutic applications, including the prediction of responsiveness to non-invasive brain stimulation. Here, we envisage the evolution of one fractal or nonlinear measure use, to several measures applied, then use it as a feature for machine learning, and then realize that a whole cluster of biomarkers must be used to reflect the state of autonomic profile, which then can be used for ontology-based application profiles that can be machine-actionable. In addition, we discuss the fractal and fractional description of transport processes, which offer innovative improvement for a much more accurate description of physiological reality as a prerequisite for further modeling: for example, this is needed for digital twins to support the clinical translation of fractal analysis for personalized medicine. In essence, if one is trying to mathematically describe or quantify structures or processes in human physiology, fractal and fractional are the supreme and adequate approach to accurately model that reality. Full article

Background: Age-related differences in neuromuscular coordination during multi-joint tasks are reported, but phase-specific evidence during maximal sprinting is limited. Aim: The aim of this study was to investigate phase-specific age differences in agonist–antagonist coordination of the biarticular thigh muscles during 50 m sprinting. Methods: Thirty-eight healthy trained track athletes (Adults: n = 21, age = 23.32 ± 2.98 years; Adolescents: n = 17, age = 13.65 ± 0.76 years) performed maximal 50 m sprints over force plates. Bilateral rectus femoris (RF) and biceps femoris (BF) sEMG and ground reaction forces were recorded; each stride was segmented into seven phases, and an RF–BF co-contraction index (CCI) was calculated per phase. Between-group differences in phase mean CCI were tested (α = 0.05) and quantified with Hedges’ g. Speed- and frequency-dependent modulation of CCI was evaluated using linear mixed-effects models (LME; random intercepts for participant) with Frequency × Group and Speed × Group interaction terms; ordinary least squares (OLS) fits on stride cycle-level group means were descriptive. Linear and single-breakpoint segmented models were compared using the corrected Akaike information criterion (AICc) and Akaike weights. Results: Adolescents showed higher CCI in contact (right: Adults 0.09 ± 0.05 vs. Adolescents 0.13 ± 0.07, g = 0.68; left: Adults 0.08 ± 0.04 vs. Adolescents 0.12 ± 0.06, g = 0.84) and propulsive phases (right: Adults 0.08 ± 0.05 vs. Adolescents 0.13 ± 0.08, g = 0.68; left: Adults 0.07 ± 0.04 vs. Adolescents 0.12 ± 0.07, g = 0.84; p < 0.05 for both legs in both phases). LME identified Frequency × Group interactions in the stride cycle (ΔSlope = 0.10, p < 0.001) and late swing (ΔSlope = 0.12, p < 0.05) and a Speed × Group interaction in mid swing (ΔSlope = 0.01, p < 0.05). Mid swing showed a positive CCI–speed/frequency relationship in both groups, whereas across most other phases Adults downregulated CCI as speed/frequency increased while Adolescents tended to increase CCI. Model selection supported phase-dependent single-breakpoint patterns, with breakpoints around 2.19–2.21 Hz and 6.11–9.51 m·s⁻¹ in Adults and around 2.11 Hz and 7.13–7.59 m·s⁻¹ in Adolescents. Conclusions: Maximal sprinting revealed phase-specific age differences in BF–RF co-contraction and its scaling with speed/frequency, which may help guide age-informed monitoring and training considerations in developing athletes. Full article

This paper presents a bench-top occupational power-assist robot aimed at reducing biomechanical effort during repetitive material handling. The prototype adopts a SCARA-like structure with three degrees of freedom and provides assistance on the vertical (z) axis through a three-phase brushless DC (BLDC) motor driven in field-oriented control with inner-loop current regulation. The user interacts with the robot through a single handle-mounted load cell. The measured interaction force is converted, via a calibration-based mapping, into a motor current reference that enforces a prescribed force-sharing ratio. In this way, the drive’s embedded current loop acts as the low-level torque regulator, and the system can share gravitational and inertial loads without additional environment force sensing or explicit high-level impedance/admittance dynamics. A coupled electro-mechanical model is derived and used to select the assistance gain and to verify feasibility in simulation. A pilot experimental campaign with eight participants and two payloads (0.5 kg and 1.5 kg) was carried out on sinusoidal and random tracking tasks. With assistance enabled, the operator contribution was reduced to about 15% of the total load, and the mean bicep brachii EMG amplitude decreased by about 60%, while tracking accuracy was generally preserved and often improved. Full article

This study aims to develop a “sensing-actuation integrated” intelligent pressurized thigh band to assist the quadriceps, indirectly alleviate knee joint load, and achieve high-precision recognition of movement modes. The system comprises a portable integrated controller and a textile-integrated flexible pneumatic actuator. Experiments were conducted to evaluate the effects of different air bladder pressure conditions on metabolic rate and muscle activity. Simultaneously, pneumatic data corresponding to six common activities were collected, and a lightweight deep learning model was developed to enable high-precision motion classification. Finally, the model was deployed to an embedded platform to demonstrate its application potential. Results indicate that appropriate air bladder pressure significantly reduces quadriceps muscle activation and average metabolic cost. Furthermore, the deep learning model achieved 99.17% accuracy in recognizing the six activities and was successfully deployed to the embedded platform. This study validates the effectiveness of the intelligent pressurized thigh band in improving locomotor performance under static pressures and demonstrates the potential of air bladder pressure variations as a proxy indicator for movement intent for future closed-loop control. Full article