Journal of Electromyography and Kinesiology最新文献

Stochastic resonance suggests that adding an optimal level of noise can enhance a weak signal, making it detectable. This report presents a secondary analysis of data from Gavriilidou et al. (2025) examining how noisy galvanic vestibular stimulation (nGVS) affects postural control. A k-nearest neighbor (KNN) classifier was used to distinguish center-of-pressure (CoP) trajectories recorded from healthy young adults standing on a firm surface with feet together and eyes closed. CoP data were analyzed using seven time-domain variables and 84 time-frequency bandwidths in the forward-backward and side-to-side directions. Three time-domain and two time-frequency features were selected for classification. Model accuracy was evaluated for differentiating among stimulus intensities (% perceptual threshold), noise types (Pink or White), and responsiveness to the perturbation. Classification accuracy exceeded 96% for all conditions, indicating distinct CoP patterns. The model further distinguished participants who did or did not exhibit a stochastic-resonance response to nGVS. SHapley Additive exPlanation analysis revealed that feature contributions were greater under White-noise stimulation. These findings demonstrate that nGVS systematically modulates postural control and that machine learning can effectively capture its condition-specific influence on balance dynamics.

Background: Carpal tunnel syndrome (CTS) is the most common entrapment neuropathy, and electrophysiological assessment remains the diagnostic gold standard.

Objective: This study aimed to determine the diagnostic performance of palmar sensory latency (PSL) in CTS using receiver operating characteristic (ROC) analysis and to examine its association with clinical severity.

Methods: A total of 136 upper limbs from 98 individuals evaluated for suspected CTS were retrospectively analyzed. Standard motor and sensory nerve conduction studies of the median nerve were performed, and PSL was obtained by stimulating the palm and recording from the index finger. ROC curve analysis was used to establish diagnostic cut-off values, sensitivity, and specificity.

Results: Among the examined limbs, 38 were diagnosed with CTS and 98 were normal. The optimal PSL cut-off was identified as ≥ 1.61 ms, yielding 84.2% sensitivity and 93.8% specificity (AUC = 0.87). PSL also showed a significant correlation with the Boston Carpal Tunnel Questionnaire (BCTQ) functional scores (r = 0.46, p < 0.01).

Conclusion: Palmar sensory latency is a highly sensitive and specific electrophysiological parameter for CTS diagnosis. Incorporating PSL into standard nerve conduction protocols may enhance early detection and diagnostic precision in clinical neurophysiology practice.

Previous studies have shown sex differences in muscle activation of the quadriceps and hamstrings with fatigue. However, whether these differences are present in a repeated sprint exercise (RSE) cycling task is unknown. Twenty (10 females) cyclists performed an RSE of 9 × 30 s bouts (1.5 min active rest in between) on their personal bike on a trainer, with instructions to produce as many watts as possible. Surface electromyography (EMG) of five right-side quadricep and hamstring muscles was recorded, with activation amplitude (RMS) calculated over each bout. Results show a general decrease with time, with more time-based fluctuations in females. Vastus medialis (VM; p < 0.001) and lateralis (VL; p = 0.004), biceps femoris (p = 0.013), rectus femoris (p < 0.001), and semitendinosus (p = 0.002) showed females only having time-based activation decreases over time. The VM:VL activation ratio showed females and males having different rates of increasing ratios. Results indicate sex and time-dependent quadricep and hamstring muscles activation during an RSE. Females' greater modulation may reflect increased need to stabilize the knee against stress, which if uncontrolled, could represent an injury risk. This knowledge can be used to identify needs for sex-specific injury-prevention approaches for cyclists.

Understanding how close an individual is to muscular failure during exercise can be used to personalize resistance training dynamically. We propose a deep learning approach to estimate proximity to failure in real-time from surface electromyography (sEMG) signals, supported by a novel dataset of 192 recordings collected from 12 participants performing isometric biceps brachii holds to failure. The recorded sEMG signals were preprocessed and converted into spectrogram windows using the Short-Time Fourier Transform. A continuous Proximity to Failure Index (PFI), defined as the elapsed percentage of the hold duration, was assigned to each window based on its corresponding position in time. Several deep learning models, including a multilayer perceptron, a Transformer, and recurrent neural networks, were trained to predict PFI values from the spectrograms and compared against linear and support vector regression baselines. Model performance was evaluated using leave-one-out cross-validation. All deep learning models outperformed the baselines, with the long short-term memory network achieving the lowest mean squared error (49.44±18.34) across participants. This work demonstrates that proximity to muscular failure can be estimated from sEMG signals during isometric holds, offering a basis for developing real-time biofeedback systems that adapt resistance training according to electromyographic activity.

Altered knee joint loading in early-stage osteoarthritis may accelerate cartilage degeneration and symptom progression. This cross-sectional observational study investigates differences in knee joint pathomechanics between 22 patients with early-stage post-traumatic knee osteoarthritis (PTOA), 26 patients with early-stage non-traumatic knee osteoarthritis (NTOA) and 20 age-/gender-matched healthy controls. Participants walked barefoot at self-selected speeds while marker data (100 Hz), ground reaction forces (1000 Hz), and surface electromyography (1000 Hz) were recorded. electromyography-informed musculoskeletal simulations estimated knee contact forces (KCF) and joint mechanics. Group differences in these outcomes and measured muscle activations were assessed via Statistical Parametric Mapping t-tests. Analysis of Covariance investigated the influence of covariates (age, gender, gait speed, alignment, strength). Early-stage NTOA and PTOA patients exhibit similar KCF and joint mechanics. Both patient groups exhibited significantly reduced second KCF peaks compared to controls (p = 0.019, Δmean NTOA - controls = 1.07 BW, Δmean PTOA - controls = 0.98 BW), suggesting knee underloading, in contrast to the joint overload typically seen in established OA. Additionally, gait speed significantly influenced KCF peaks. These findings suggest that early OA may be characterized by cartilage underloading. Longitudinal studies and more demanding tasks may reveal phenotype-specific biomechanical distinctions and also confirm continuous cartilage underloading in disease progression.