Search Results (1,103)

Despite growing evidence on the adaptive movement patterns that horses adopt during water treadmill (WT) exercise, underlying adaptations in muscle activity remain uninvestigated. This feasibility study aimed to develop a method for the synchronous measurement of muscle activity and movement of horses during WT exercise. Combined surface electromyography (sEMG) (2000 Hz) from selected hindlimb (biceps femoris, gluteus medius, tensor fascia latae) and epaxial (longissimus dorsi) muscles, and three-dimensional kinematic (200 Hz) data from the back and pelvis of one (n = 1) horse were collected during overground (OG), dry treadmill (TM), and WT walking conditions. Statistical parametric mapping evaluated differences in time- and amplitude-normalised sEMG and thoracolumbar and pelvis kinematic waveforms between conditions. Distinct, significant (p < 0.05) adaptations in hindlimb and epaxial muscle activation patterns and axial and pelvic kinematics, were observed in this horse across exercise conditions. Adaptive muscle activity was most pronounced in this horse during WT, compared to OG walking. These findings demonstrate the feasibility of this method, which combines sEMG and motion capture technologies to synchronously quantify equine movement and muscle activation patterns during WT exercise. This justifies the replication of this work in a larger sample of horses to inform evidence-based training and rehabilitation programmes. Full article

Visceral pain in Irritable Bowel Syndrome (IBS) is difficult to evaluate objectively due to its complex physiological nature and lack of reliable biomarkers. Given the complexity of IBS, a multimodal physiological monitoring approach, combining electrodermal activity (EDA), electrocardiogram (ECG), and surface electromyography (sEMG), offers a promising approach to capture the autonomic and muscular responses linked to visceral pain. However, no existing wearable device supports simultaneous EDA, ECG, and sEMG acquisition from the abdomen in a format suitable for long-term, real-world use. This study presents the development and validation of a novel wearable belt for concurrent ECG, sEMG, and EDA monitoring, with EDA measured at both the torso and wrist. The system was built using modified BITalino platforms with custom-fabricated reusable electrodes and Bluetooth connectivity for real-time smartphone display. Signal quality was validated against laboratory-grade systems in 20 healthy participants during a four-stage protocol involving cognitive, orthostatic, muscular, and combined stress tasks. Time and frequency-domain analyses showed high correlations and comparable spectral features across all modalities. The belt maintained stable skin contact even during movement-intensive tasks. By enabling anatomically targeted, multimodal data acquisition, this wearable system supports real-world visceral pain assessment in IBS and is ready for deployment in ambulatory and home-based monitoring scenarios. Full article

Surface electromyographic (sEMG) signals result from the interaction between motor unit action potentials (MUAPs) and neural spike trains, yet how specific features of spike timing shape the sEMG spectrum is not fully understood. Using a simplified convolutional model, we simulated sEMG by combining synthetic spike trains with MUAP templates, varying firing rate, temporal jitter, and motor unit synchronization to examine their effects on spectral characteristics. Rather than addressing a particular experimental condition such as fatigue or workload, the main goal of this study is to provide a framework that clarifies how variability in neural timing and muscle properties affects the observed sEMG spectrum. We introduce extractability indices to measure how clearly neural activity appears in the spectrum. Results show that MUAPs act as spectral filters, reducing components outside their bandwidth and limiting the detection of high firing rates. Temporal jitter spreads spectral energy and blunts frequency peaks, while moderate synchronization improves spectral visibility, partially countering jitter effects. These findings offer a reference for interpreting how neural and muscular factors shape sEMG signals, supporting a more informed use of spectral analysis in both experimental and applied neuromuscular studies. Full article

Objectives: This study aimed to evaluate the effect of a superior ankle traction manipulation on the strength and electrical activity (surface EMG) of the peroneus longus, gluteus medius, and tensor fasciae latae muscles in healthy young adults. Methods: In total, 30 healthy participants (26 men and 4 women) were enrolled in a prospective, randomized, double-blind, controlled study. Participants were randomly assigned to a Manipulation or Sham group. Muscle activity was recorded using surface EMG, and isometric strength was assessed with a Biodex dynamometer. EMG signals were normalized to session-specific maximal voluntary isometric contractions (MVIC) and expressed as %MVIC for amplitude and median frequency. Baseline differences were examined with Welch’s t-tests. The primary analysis used analysis of covariance (ANCOVA) on POST values adjusted for PRE, with partial eta squared (η²_p) as an effect size. Change-score comparisons (Δ = POST − PRE) and Hedges-corrected Cohen’s d were reported as sensitivity analyses. False discovery rate (FDR) correction was applied across outcomes. Results: No significant between-group differences were observed after adjustment for baseline in any %MVIC amplitude or median frequency outcome (p > 0.05, all FDR-adjusted q > 0.05). Within-group analyses showed small, nonsignificant changes in both groups, with the Manipulation group tending toward slightly greater increases in peroneus longus %MVIC amplitude (Δ = +3.1%, p = 0.033, d = 0.79, not significant after FDR correction). Descriptive data indicated similar PRE and POST values across groups for all muscles. Conclusions: When EMG activity is expressed relative to MVIC and baseline differences are controlled, a single superior ankle traction manipulation does not produce statistically significant acute changes in peroneus longus, gluteus medius, or tensor fasciae latae activity compared with a sham procedure. These findings suggest that previously reported differences may have reflected unadjusted baseline variability rather than true intervention effects. Full article

Deaf-blindness, particularly in progressive conditions such as Usher syndrome, presents profound challenges to communication, independence, and access to information. Existing tactile communication technologies for individuals with Usher syndrome are often limited by the need for close physical proximity to trained interpreters, typically requiring hand-to-hand contact. In this study, we introduce a novel, cloud-based, AI-assisted gesture recognition and haptic communication system designed for long-term use by individuals with Usher syndrome, whose auditory and visual abilities deteriorate with age. Central to our approach is a wearable haptic interface that relocates tactile input and output from the hands to an arm-mounted sleeve, thereby preserving manual dexterity and enabling continuous, bidirectional tactile interaction. The system uses surface electromyography (sEMG) to capture user-specific muscle activations in the hand and forearm and employs lightweight, personalized convolutional neural networks (CNNs), hosted on a centralized server, to perform real-time gesture classification. A key innovation of the system is its ability to adapt over time to each user’s evolving physiological condition, including the progressive loss of vision and hearing. Experimental validation using a public dataset, along with real-time testing involving seven participants, demonstrates that personalized models consistently outperform cross-user models in terms of accuracy, adaptability, and usability. This platform offers a scalable, longitudinally adaptable solution for non-visual communication and holds significant promise for advancing assistive technologies for the deaf-blind community. Full article

Background: Children with autism spectrum disorder (ASD) frequently show postural abnormalities and elevated muscle tone, which can hinder participation in education and rehabilitation. Evidence on the immediate physiological effects of standardized multisensory environments is limited. Objective: To evaluate feasibility, safety and short-term physiological/postural responses to an automated multisensory smart relaxation system in children with severe ASD. Methods: In a single-session pilot across three sites, 30 children (27 boys; 6–16 years) underwent pre–post postural observation and bilateral surface EMG of the upper trapezius, biceps brachii and rectus abdominis. The system delivered parameterized sound, vibration, and mild heat. EMG was normalized to a quiet-sitting baseline. Results: The intervention was well tolerated with no adverse events. Most children sat independently (25/30; 80%) and a majority stood up unaided after the session (24/30; 76.9%). Postural profiles reflected common ASD features (neutral trunk 76%, forward head 52%, rounded/protracted shoulders 46%), while limb behavior was predominantly calm (73%). Normalized EMG amplitudes were low, with no significant pre–post changes and no meaningful left–right asymmetries (all p > 0.05; Cohen’s d < 0.20), indicating physiological calmness rather than tonic co-contraction. Conclusions: A single session with a smart multisensory relaxation system was safe, feasible, and physiologically calming for children with severe ASD, without increasing postural or muscular tension. The platform’s standardization and objective monitoring support its potential as a short-term calming adjunct before therapy or classroom tasks. Larger, gender-balanced, multi-session trials with behavioral outcomes are warranted. Full article

Speed and load effects on the number and type of pair interactions between six elbow and shoulder muscles or muscle (m.) heads were evaluated by the intercriteria decision-making method (ICrA). The surface electromyography (sEMG) signals of the m. deltoideus pars clavicularis (Dcla), m. deltoideus pars spinata (Dspi), m. biceps brachii (BB), m. triceps brachii caput longum (TB), m. brachialis (BR), and m. anconeus (AN) of ten healthy subjects were recorded. The data was collected during cycling movements (CMs) for continuous flexions and extensions in the elbow joint in the horizontal plane. The CMs were performed with and without an added load at four different speeds. The obtained sEMG data were subjected to the ICrA to identify muscle activity and speed correlations. The ICrA results demonstrate that added load resulted in a higher number of consonance relations between muscle activities. Positive consonance (PosC) appears between the Dcla-Dspi, Dspi-BR, BB-BR, and TB-BR criteria pairs for the loaded flexion phases. When extension is in the focus, Dcla-BB is in a consonance relation for no loaded phases, while for the loaded ones, five muscle pairs, namely Dcla-BB, Dcla-BR, Dspi-BR, BB-BR, and TB-BR, hit PosC. Also, the most correlations are found for the fastest phase (1 s) of flexion and extension, regardless of the load. Additionally, correlation dependencies between the two faster (Sp2-Sp1) and the two slower speeds (Sp10-Sp6) were found. Full article

In patients with respiratory failure, mechanical ventilation aims to balance respiratory muscle loading and gas exchange. The interplay between the ventilator and the respiratory muscles is an increasingly recognized factor in tailoring ventilatory support. Surface electromyography (sEMG) offers a non-invasive modality to monitor the respiratory muscles. The sEMG signal, however, requires elaborate processing, which is limitedly standardized and documented. This paper presents the Respiratory Surface Electromyography (ReSurfEMG) package, an open-source Python package for respiratory sEMG analysis developed to address these challenges. ReSurfEMG integrates denoising, feature extraction, and quality assessment in one dedicated library. The effects of over- and under-filtering were compared to ReSurfEMG default settings regarding waveform duration, time-to-peak, amplitude, electrical time product (ETP), pseudo-slope, pseudo-signal-to-noise ratio (SNR), area under the baseline (AUB), and bell-curve error. Under-filtering increased amplitudes (+21%) and ETPs (+10%). Over-filtering smoothed sEMG waveforms, reducing amplitude (−58%), ETP (−39%), and pseudo-slope (−49%), while waveform duration and time-to-peak increased. Default ReSurfEMG settings provided the highest SNRs with similar or lower AUBs and bell-curve errors. The ReSurfEMG library integrates advanced methods dedicated to respiratory sEMG analysis. Systematic assessment using ReSurfEMG showed that signal processing settings affect sEMG features. ReSurfEMG enables reproducible signal processing, facilitating the standardization of respiratory sEMG analysis. Full article

Parkinson’s disease (PD) is one of the fastest-growing neurodegenerative disorders; its cardinal motor signs—tremor, bradykinesia, and rigidity—substantially impair quality of life. Conventional clinician-rated scales can be subjective and exhibit limited interrater reliability, underscoring the need for objective and reliable quantification. We present an integrated evaluation framework that leverages surface electromyography (sEMG) with multimodal sensing. For representation learning, we combine time–frequency descriptors with Mini-ROCKET features. Grading is performed by an sEMG-based Unified Parkinson’s Disease Rating Scale (UPDRS) model (LDA-SV) that produces per-segment probabilities for ordinal scores (0–3) and aggregates them via soft voting to assign item-level ratings. Participants completed a standardized protocol spanning gait, seated rest, and upper-limb tasks (forearm pronation–supination, finger-to-nose, fist clench, and thumb–index pinch). Using the aforementioned dataset, we report task-wise performance with 95% confidence intervals and compare the proposed model against CNN, LSTM, and InceptionTime using McNemar tests and log-odds ratios. The results indicate that the proposed model outperforms the three baseline models overall. These findings demonstrate the effectiveness and feasibility of the proposed approach, suggesting a viable pathway for the objective quantification of PD motor symptoms and facilitating broader clinical adoption of sEMG in diagnosis and treatment. Full article

Aim: The aim of this study was to develop and test a surface electromyography (sEMG) assessment protocol to characterise the activity of the extrinsic laryngeal muscles (suprahyoid and infrahyoid) during phonatory tasks and vocal techniques. Methodology: The protocol of assessment was based on electromyographic assessment guidelines and on clinical voice evaluation needs and was tested in six healthy adults with no vocal disorders. Surface electromyographic activity of suprahyoid and infrahyoid muscles was acquired during different reference tasks (rest, reading, maximum contractions) and six vocal tasks, including nasal sounds, fricatives, and semi-occluded vocal tract exercises. A laryngeal accelerometer was used for detecting the beginning and end of each exercise. The average activity during each task was normalised by the signal obtained in the incomplete swallowing task for the SHM and by the sniff technique for the IHM. Results: The range of activation values varied across tasks, with higher percentages observed in plosive production and in the “spaghetti” technique, while nasal and fricative sounds tended to show lower activation values within the group. A consistent pattern of simultaneous activation of suprahyoid and infrahyoid muscles was observed during phonation. Conclusions: The protocol proved potential for clinical application in speech–language pathology as it enabled the characterisation of muscle activity in determinant muscles for vocal function. Larger samples and further validation of the time-marking system are needed. This study provides a foundation for integrating sEMG measures into functional voice assessment. Full article

Recent advances in exoskeleton-assisted rehabilitation have highlighted the significance of lower limb movement intention recognition through deep learning. However, discrete motion phase classification and continuous real-time joint kinematics estimation are typically handled as independent tasks, leading to temporal misalignment or delayed assistance during dynamic movements. To address this issue, this study presents iTransformer-DTL, a dual-task learning framework with an improved Transformer designed to identify end-to-end locomotion modes and predict joint trajectories during sit-to-stand transitions. Employing a learnable query mechanism and a non-autoregressive decoding approach, the proposed iTransformer-DTL can produce the complete output sequence at once, without relying on any previously generated elements. The proposed framework has been tested with a dataset of lower limb movements involving seven healthy individuals and seven stroke patients. The experimental results indicate that the proposed framework achieves satisfactory performance in dual tasks. An average angle prediction Mean Absolute Error (MAE) of 3.84° and a classification accuracy of 99.42% were obtained in the healthy group, while 4.62° MAE and 99.01% accuracy were achieved in the stroke group. These results suggest that iTransformer-DTL could support adaptable rehabilitation exoskeleton controllers, enhancing human–robot interactions. Full article

Background: Wireless surface electromyography (sEMG) enables the investigation of neuromuscular control in realistic sports settings; however, ensuring reliable signal acquisition during sprinting remains challenging. This study examined the feasibility of continuous wireless EMG recording in sprinting athletes and evaluated their agonist–antagonist coordination patterns. Methods: Ten trained sprinters performed four maximal 50-m sprints on a force plate–equipped track. sEMG was recorded from the biceps femoris (BF), rectus femoris (RF), soleus (Sol), and tibialis anterior (TA) under two receiver configurations: fixed-receiver condition (FRC) and mobile-receiver condition (MRC). Integrated EMG, kinematics, and cross-correlation analyses were performed on a stride-by-stride basis. Results: Continuous high-quality EMG was feasible under MRC, highlighting the practical importance of maintaining receiver proximity in sprint experiments. BF activity during the late swing phase correlated positively with sprint velocity, supporting the performance relevance of pawing. BF/RF interactions varied substantially across individuals, whereas Sol/TA were consistently coactivated, indicating ankle stabilization. Conclusions: Wireless EMG enables reliable in-field monitoring of sprinting athletes, revealing both individualized and shared coordination strategies relevant to performance and injury prevention in athletes. Full article

The implementation of surface electromyographic (sEMG) signals in the interaction between human beings and machines is an important line of research. In the system of human–machine interaction, continuous-motion-estimation-based control plays an important role because it is more natural and intuitive than pattern recognition-based control. In this paper, we propose a parallel network consisting of a CNN with a multi-head attention mechanism and a BiLSTM (bidirectional long short-term memory) network to improve the accuracy of continuous motion estimation. The proposed network is evaluated in the Ninapro dataset. Six finger movements of 10 subjects were tested in the Ninapro DB2 dataset to evaluate the performance of the neural network and calculate the PCC (Pearson Correlation Coefficient) between the predicted joint angle sequence and the actual joint angle sequence. The experimental results show that the average accuracy (PCC) of the proposed network reaches 0.87 ± 0.02, which is significantly better than that of the BiLSTM network (0.79 ± 0.04, p < 0.05), CNN-Attention (0.80 ± 0.01, p < 0.05), CNN (0.70 ± 0.03, p < 0.05), CNN-BiLSTM (0.83 ± 0.02, p < 0.05), and TCN (0.76 ± 0.05, p < 0.05). It is worth noting that in this work, we extract multiple features from the raw sEMG signals and fuse them. We found that better continuous estimation accuracy can be achieved using multi-feature sEMG data. The model proposed in this paper skillfully integrates the convolutional neural network, multi-head attention mechanism, and bidirectional long short-term memory network, and its performance has good stability and accuracy. The model realizes more natural and accurate human–computer interaction. Full article

Hamstring injuries are frequent in sports, often linked to eccentric overloading during sprinting. While eccentric strengthening, like Nordic curls and hip extensions, is common, the impact of exercise symmetry (unilateral vs. bilateral) on neuromuscular control remains unclear. This study aimed to investigate regional/task-specific neuromuscular strategies during unilateral and bilateral eccentric loading of the same exercises. Twenty-five healthy and physically active young men (age: 24.52 ± 3.82 years; height: 175.53 ± 5.44 cm; weight: 72.06 ± 7.44 kg) were recruited based on physical activity screening, with the exclusion criteria including recent lower limb injuries. Participants performed unilateral and bilateral curls and extensions with surface electromyography on hamstrings, gluteus maximus, and trunk stabilisers. Parameters like root mean square and median frequency were extracted and statistically compared. Unilateral execution generally elicited higher muscle activation, particularly in middle hamstring regions (30.65% to 38.38% in RMS, r = −0.84 to −0.77, p_FDR < 0.001). Frequency differences revealed region-specific neuromuscular strategies. Intra-hamstring comparisons revealed significantly higher median frequencies in the BF50 and ST30 regions at their respective anatomical locations (dz = −1.90 to 1.34, all p_FDR < 0.001). These findings suggest that exercise symmetry and anatomical specialisation jointly shape neuromuscular control, with implications for designing eccentric training to reduce injury risk. Full article

Robotic devices have become increasingly important in upper-limb rehabilitation, as they assist therapists, improve treatment efficiency, and enable personalised therapy. However, the lack of standardised protocols and integrative tools limits their widespread adoption and effectiveness. To address these challenges, a robotic framework was developed for upper-limb rehabilitation in patients with acquired brain injury (ABI). The framework is designed to be adaptable to various ROS-compatible collaborative robots with admittance control and potentially adaptable to other types of control, and also integrates kinematic and electrophysiological (EMG) metrics to monitor patient performance and progress. It combines data acquisition through EMG and robot motion sensors, gamification elements to enhance engagement, and configurable robot control modes within a unified software platform. A pilot evaluation with eight healthy subjects performing upper limb movements on an ROS-compatible robot from the UR family demonstrated the feasibility of the framework’s components, including robot control, EMG acquisition and synchronization, gamified interaction, and synchronised data collection. User performance through all levels remained below the controller limits of force and velocity thresholds even in the most resistive damping. These results support the potential of the proposed framework as a flexible, extensible, and integrative tool for upper-limb rehabilitation, providing a foundation for future clinical studies and multi-platform implementations. Full article