Search Results (765)

This study proposes a non-invasive, simplified muscle force estimation model (NSMFEM) designed for elderly individuals and stroke patients under slow walking conditions. The model estimates lower limb muscle forces dynamically using only kinematic parameters—with real-time muscle fiber length as the key variable—thus avoiding the limitations of traditional surface electromyography (sEMG)-based approaches such as environmental interference, signal noise, and difficulty in obtaining deep muscle sEMG. A personalized Digital Twin Musculoskeletal Model (DTMSM) was constructed by scaling a reference kinematic model and calibrating muscle origin/insertion markers based on individual anthropometry. Muscle architecture indices were derived from a multiple regression model with publicly available anatomical data. Twelve elderly subjects (eight healthy ESND and four post-stroke ESP) were evaluated at varying walking speeds. Results at slow speeds (X-slow and slow) show strong Pearson correlations between NSMFEM predictions and reference data for the majority of nine representative lower limb muscles (e.g., TFL, Iliacus, Pectineus, Tib_Ant, Soleus); passive forces of TFL, Iliacus, and Vas_Int also correlate strongly. As speed rises, correlations for some muscles (e.g., Vas_Int, Tib_Post) decline, reflecting the growing influence of segmental acceleration and muscle activation—factors omitted in the model. For stroke patient gait (ESP), Spearman analysis indicates maintained strong correlations for affected side muscles Glut_Max1, TFL, Pectineus, and Soleus, supporting the model’s utility in stroke rehabilitation assessment. Overall, NSMFEM offers a practical, sEMG free method for non-invasive dynamic muscle force estimation in slow walking elderly and post-stroke populations, aiding functional assessment and personalized rehabilitation planning. Future efforts will aim to incorporate muscle activation corrections to extend the model to faster walking speeds. 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

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: 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

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 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

Non-contact anterior cruciate ligament (ACL) injuries cause substantial time loss in female football. Although altered lower-limb muscle excitation is a modifiable risk factor, the reliability of surface electromyography (sEMG) during dynamic tasks in female players remains uncertain. This repeated-measures reliability study examined sEMG during a single-leg jump landing (LAND) and 90° sidestep cut (CUT) in 16 second-tier English female footballers. We evaluated reliability across: (1) within- versus between-session measures; (2) mean versus peak amplitudes; (3) pre-initial contact (PRE-IC) versus post-initial contact (POST-IC) phases; and (4) 10 ms versus 50 ms smoothing windows. Reliability was quantified using intraclass correlation coefficient (ICC[2,k]) and absolute measurement error. Within-session ICCs were moderate to excellent (LAND 0.61 to 0.95; CUT 0.68 to 0.96), whereas between-session ICCs varied from poor to excellent (LAND −0.48 to 0.94; CUT −0.08 to 0.93). Mean amplitudes showed marginally higher ICCs and lower absolute error than peaks. Phase-specific patterns were task-dependent: PRE-IC was more reliable in LAND, whereas POST-IC was more reliable in CUT. Practitioners should prioritize within-session comparisons using mean amplitudes, and the most reliable task-specific phase is recommended. Between-day application warrants caution, as the consistently lower reliability demonstrated may reflect task variability and/or physiological fluctuations rather than the sEMG method alone. Full article

Surface electromyography (sEMG) signals are inevitably affected by noise during acquisition, thereby degrading signal quality and analytical reliability. Most existing denoising methods combine signal decomposition with thresholding, and their performance depends on empirically set decomposition parameters and threshold estimation. However, in high-rate repetitive motions such as rapid keystrokes, sustained high-duty-cycle muscle activation biases universal-threshold noise estimation, leading to unreliable thresholds. To overcome these issues, an sEMG denoising method that integrates the Walrus Optimizer (WO) with Variational Mode Decomposition (VMD) is proposed. WO is employed to optimize key VMD parameters, including the number of modes K and the penalty factor $\alpha$. Based on this method, an improved threshold estimation strategy is developed to accommodate high-duty-cycle sEMG during rapid keystrokes. It reduces thresholding-induced over-attenuation of meaningful myoelectric components. The dataset included 18 participants with sEMG recorded from six muscles during rapid keystroke tasks (10 trials per participant; 20 keystrokes per trial). Across input signal-to-noise ratios (SNRs) of 0, 5, 10, 15 dB, the proposed method achieved a median SNR improvement (ΔSNR) ranging from 2.75 to 6.65 dB and a median root-mean-square error (RMSE) reduction rate (ΔRMSE%) ranging from 27% to 53%, while maintaining spectral fidelity with a median of median frequency variation rate (ΔMDF%) below 3.48%.These results indicate that the proposed method provides an efficient and reliable solution for sEMG signal processing in rapid keystroke analysis. Full article

Research on muscle fatigue during dynamic movement using surface electromyography (sEMG) constitutes a significant challenge within biomechanics. Despite a degree of standardization, measurements and their resultant findings continue to attract considerable debate, attributable to factors such as skin impedance, perspiration, and electrode displacement, as well as subjective fatigue perception. Further questions remain regarding signal normalization and the selection of appropriate analytical methodologies. Recent years have witnessed notable progress in dynamic fatigue research, highlighting the limitations of classical metrics (e.g., EMG Median Frequency) and introducing time–frequency methods, such as the wavelet transform (WT), which are better equipped to handle signal non-stationarity. Interest has also expanded to include non-linear metrics (e.g., entropy) and the analysis of multiple signals (EMG, accelerometers, fNIRS, EEG). The inherent complexity of conducting studies under conditions that approximate real-world sporting disciplines requires the consideration of the influence of various confounding factors. The judicious selection of relevant physical activities and the rigorous validation of the measurement apparatus are paramount for the accurate execution of the calculations. Current research is substantially predicated on artificial intelligence (AI) algorithms. The synergistic application of AI with wavelet transform, particularly in the decomposition and extraction of EMG signals, demonstrates efficacy in fatigue detection. Nevertheless, the full realization of these potential mandates requires further investigation into system generalization, the integration of data from multiple sensors, and the standardization of protocols, coupled with the establishment of publicly accessible datasets. This article delineates selected guidelines and challenges pertinent to the planning and execution of research on muscle fatigue in dynamic movement, focusing on activity selection, equipment validation, EMG signal analysis, and AI utilization. Full article

Background: The aim of this observational preliminary study is to detect any changes in body mass, muscle strength and characteristics of the pectoralis major muscle in women who have undergone breast surgery treatments. Methods: Instrumental assessments, completed before surgery and after 60 and 120 days, included sonoelastography, dynamometric examination and surface electromyography (sEMG) of the pectoralis major muscle, hand grip test, body bioimpedance analysis; the DASH (Disability of the Arm, Shoulder and Hand) questionnaire and pain assessment using the NRS (Numerical Rating Scale). Results: An initial increase in weight and fat mass was observed, followed by a reduction related to the resumption of physical activity stimulated by physiotherapy and medical support. The IC (intracellular)/EC (extracellular) ratio showed an increase in extracellular fluids in the final phase, indicative of possible water retention and early oedema. Muscle strength and DASH scores showed a functional decline, which may be explained by reduced physical activity and the direct involvement of the pectoral muscle in surgical and radiotherapy procedures. Sonoelastography showed color variations suggestive of changes in tissue stiffness, useful for distinguishing between reinforcement processes and possible scarring. Conclusions: This multidimensional approach can be useful in the early monitoring of some tissue alterations (i.e., fat mass) as an aid to define personalized rehabilitation protocols for women who have undergone breast surgery. Full article

Background: The objective of this study was to assess the tension of selected postural muscles during the jaw-sucking movement in four body positions (standing position, all-fours position, lying on front, lying on the side). Material and Methods: The research involved 30 young adults with an average age of 22.6 ± 0.72 years. Suprahyoid, trapezius, gluteus maximus, and gastrocnemius muscles were assessed in all study participants in the standing, kneeling, and belly lying positions (prone position). Measurements were taken twice for each position: once without jaw activity and once with jaw movements simulating sucking. Muscle function was determined by measuring muscle tension using surface electromyography (sEMG). Results: Engaging jaw movements in the prone position resulted in significantly increased tension in the gastrocnemius muscle. In the all-fours position, there was a notable rise in tension in both the gastrocnemius and gluteus maximus muscles. When standing, significantly higher tension was observed in the trapezius and gluteus maximus muscles. In contrast, the side-lying position exhibited no significant changes in muscle tension. Conclusions: The study’s findings suggest that activating jaw function may affect the tone of the gastrocnemius muscle in both prone and quadrupedal positions. In contrast, there were no clear or statistically significant changes observed in the tone of trapezius muscles in either position, while, for the tension of the gluteus medius muscle, variability was shown only in the all-fours position. Full article

This study proposes a data quality-driven channel selection methodology to improve hand gesture recognition performance in multi-channel wearable Human–Machine Interface (HMI) systems. The methodology centers around calculating (i) five data quality indices for both surface electromyography (sEMG) and pressure-based force myography (pFMG) signals and (ii) establishing a relationship between these data quality indices and the accuracy of gesture recognition for applications typified by prosthetic hand control. Machine learning (ML)-based and correlation-based methods were used to select three optimal channel/pair configurations from an eight-channel/pair system. Evaluations on the UOW and Ninapro DB2 datasets showed that the proposed methods consistently outperformed random channel selection, with the ML-based approach achieving the best results (76.36% for sEMG, 71.59% for pFMG, and 88.2% for fused sEMG-pFMG on the UOW dataset and 70.28% on Ninapro DB2). Notably, using three pairs of strategically selected sEMG-pFMG channels generated 88.2%, which is comparable to the 88.38% accuracy obtained with a full eight-channel sEMG system on the UOW dataset, highlighting the efficacy of our channel selection methodologies. These results highlight the value of data quality indices for sensor selection and provide a foundation for developing more efficient wearable HMI systems. Full article

Existing methods for recognizing motion intent in lower limb rehabilitation robots focus on spatial feature extraction while neglecting movement continuity, thus failing to extract temporal features. This paper proposes a movement intention recognition model based on a CNN-LSTM parallel dual-stream spatio-temporal neural network, taking surface electromyography (sEMG) signals as the core data. This model concurrently extracts temporal and spatial features from sEMG signals, integrating dual-dimensional information to comprehensively explore deep signal characteristics. By overcoming the limitations of traditional single-feature extraction, it significantly enhances recognition accuracy. Experimental results from movement intention recognition studies involving multiple subjects demonstrate an average recognition accuracy of 97%, providing reliable technical support for precise intent recognition and human–robot collaborative control in lower limb rehabilitation robots. Full article