Search Results (199)

Surface electromyographic (sEMG) signal-driven joint-angle estimation plays a critical role in intelligent rehabilitation systems, as its accuracy directly affects both control performance and rehabilitation efficacy. This study proposes a continuous elbow joint angle estimation method based on time–frequency domain analysis. Raw sEMG signals were processed using the Short-Time Fourier Transform (STFT) to extract time–frequency features. A Scale Temporal–Channel Cross-Encoder (STCCE) network was developed, integrating temporal and channel attention mechanisms to enhance feature representation and establish the mapping from sEMG signals to elbow joint angles. The model was trained and evaluated on a dataset comprising approximately 103,000 samples collected from seven subjects. In the single-subject test set, the proposed STCCE model achieved an average Mean Absolute Error (MAE) of $2.96\pm {0.24}^{\circ }$, Root Mean Square Error (RMSE) of $4.41\pm {0.45}^{\circ }$, Coefficient of Determination ($R^2$) of $0.9924\pm 0.0020$, and Correlation Coefficient (CC) of $0.9963\pm 0.0010$. It achieved a MAE of ${3.30}^{\circ }$, RMSE of ${4.75}^{\circ }$, $R^2$ of 0.9915, and CC of 0.9962 on the multi-subject test set, and an average MAE of $15.53\pm {1.80}^{\circ }$, RMSE of $21.72\pm {2.85}^{\circ }$, $R^2$ of $0.8141\pm 0.0540$, and CC of $0.9100\pm 0.0306$ on the inter-subject test set. These results demonstrated that the STCCE model enabled accurate joint-angle estimation in the time–frequency domain, contributing to a better motion intent perception for upper-limb rehabilitation. Full article

The paper presents a shared control strategy that allows a human operator to physically guide the end-effector of a robotic manipulator to perform different tasks, possibly in interaction with the environment. To switch among different operational modes referring to a finite state machine algorithm, ElectroMyoGraphic (EMG) signals from the user’s arm are used to detect muscular contractions and to interact with a variable admittance control strategy. Specifically, a Support Vector Machine (SVM) classifier processes the raw EMG data to identify three classes of contractions that trigger the activation of different sets of admittance control parameters corresponding to the envisaged operational modes. The proposed architecture has been experimentally validated using a Kinova Jaco² manipulator, equipped with force/torque sensor at the end-effector, and with a limited group of users wearing Delsys Trigno Avanti EMG sensors on the dominant upper limb, demonstrating promising results. Full article

Background/Objectives: The projected rise in limb amputations highlights the need for advancements in prosthetic technology. Current transtibial prosthetic designs primarily focus on sagittal plane kinematics but often neglect both the ankle kinematics and kinetics in the coronal plane, and the metatarsophalangeal joint, which play critical roles in gait stability and efficiency. This study aims to evaluate the combined effects of compliance in the coronal plane and a flexible toe joint on prosthetic gait using non-amputated participants as a model. Methods: We conducted gait trials on ten non-amputated individuals in the presence and absence of compliance in the coronal plane and toe compliance, using a previously developed three-degree-of-freedom (DOF) prosthetic foot with a prosthetic simulator. We recorded and analyzed sagittal and coronal kinematic data, ground reaction forces, and electromyographic signals from muscles involved in the control of gait. Results: The addition of compliance in the coronal plane and toe compliance had significant kinematic and muscular effects. Notably, this compliance combination reduced peak pelvis obliquity by 27%, preserved the swing stance/ratio, and decreased gluteus medius’ activation by 34% on the non-prosthetic side, compared to the laterally rigid version of the prosthesis without toe compliance. Conclusions: The results underscore the importance of integrating compliance in the coronal plane and toe compliance in prosthetic feet designs as they show potential in improving gait metrics related to mediolateral movements and balance, while also decreasing muscle activation. Still, these findings remain to be validated in people with transtibial amputations. Full article

(1) Background: Biofeedback and neurofeedback are gaining attention as non-invasive rehabilitation strategies in Parkinson’s disease (PD) treatment, aiming to modulate motor and non-motor symptoms through the self-regulation of physiological signals. (2) Objective: This review explores the application of biofeedback techniques, electromyographic (EMG) biofeedback, heart rate variability (HRV) biofeedback, and electroencephalographic (EEG) neurofeedback in PD rehabilitation, analyzing their impacts on motor control, autonomic function, and cognitive performance. (3) Methods: This review critically examined 15 studies investigating the efficacy of electromyographic (EMG), heart rate variability (HRV), and electroencephalographic (EEG) feedback interventions in PD. Studies were selected through a systematic search of peer-reviewed literature and analyzed in terms of design, sample characteristics, feedback modality, outcomes, and clinical feasibility. (4) Results: EMG biofeedback demonstrated improvements in muscle activation, gait, postural stability, and dysphagia management. HRV biofeedback showed positive effects on autonomic regulation, emotional control, and cardiovascular stability. EEG neurofeedback targeted abnormal cortical oscillations, such as beta-band overactivity and reduced frontal theta, and was associated with improvements in motor initiation, executive functioning, and cognitive flexibility. However, the reviewed studies were heterogeneous in design and outcome measures, limiting generalizability. Subgroup trends suggested modality-specific benefits across motor, autonomic, and cognitive domains. (5) Conclusions: While EMG and HRV systems are more accessible for clinical or home-based use, EEG neurofeedback remains technically demanding. Standardization of protocols and further randomized controlled trials are needed. Future directions include AI-driven personalization, wearable technologies, and multimodal integration to enhance accessibility and long-term adherence. Biofeedback presents a promising adjunct to conventional PD therapies, supporting personalized, patient-centered rehabilitation models. Full article

Background: Musculoskeletal (MSK) pain is a leading contributor to global disability and healthcare burdens. While self-reported pain scales remain the clinical standard, they are limited by subjectivity and inter-individual variability. Therefore, objective assessment tools are increasingly sought to enhance diagnostic precision, guide treatment, and enable reproducible research outcomes. Methods: This comprehensive narrative review synthesizes evidence from physiological, behavioral, and neuroimaging approaches used to evaluate MSK pain objectively. Emphasis is placed on autonomic biomarkers (e.g., heart rate variability, skin conductance), facial expression analysis, electromyographic methods, and functional neuroimaging modalities such as fMRI and PET. Emerging applications of artificial intelligence and multimodal diagnostic strategies are also discussed. Results: Physiological signals provide quantifiable correlations of pain-related autonomic activity but are influenced by psychological and contextual factors. Behavioral analyses, including facial action coding systems and reflex testing, offer complementary, though complex, indicators. Neuroimaging techniques have identified pain-related brain patterns, yet clinical translation is limited by variability and standardization issues. Integrative approaches show promise for improving diagnostic validity. Conclusions: Objective assessment of MSK pain remains methodologically challenging but holds substantial potential for enhancing clinical diagnostics and personalized management. Future research should focus on multimodal integration, standardization, and translational feasibility to bridge the gap between experimental tools and clinical practice. Full article

Objectives: A pulsed electromagnetic field (PEMF) induces electric currents in biological tissue, enhancing muscle energy expenditure during heavy constant-load exercises. In this paper, we investigate the PEMF effect on muscular activation in male sedentary people. Methods: The surface electromyographic (EMG) activity of the right leg’s vastus medialis (RVM) and biceps femoris (RBF) muscles was recorded and analyzed. The root mean square values were normalized to the peak amplitude observed during maximal voluntary contraction. Measurements were taken at baseline (stationary seated position), during warm-up (unloaded cycling), and throughout 15 min of constant-load exercise performed at moderate intensity. Subjects performed two experimental conditions, when PEMF was turned ON versus OFF. Results: No significant difference was found during the baseline. The analysis during warm-up showed significant differences between conditions (ON vs. OFF) for both muscles (RVM p = 0.019; RBF p < 0.001). The analysis during constant-load exercise showed significant differences between conditions (ON vs. OFF) for RVM only (p = 0.002). Conclusions: This study provides evidence that PEMF stimulation acutely enhances muscle activation, primarily in the vastus medialis, with a comparatively smaller effect on the biceps femoris during moderate-intensity cycling in sedentary young men. The observed increase in EMG activity suggests that PEMF may facilitate neuromuscular excitability and muscle recruitment, potentially through mechanisms related to calcium signaling and enhanced muscle perfusion. Full article

The analysis of electrocardiogram (ECG) signals is profoundly affected by the presence of electromyographic (EMG) noise, which can lead to substantial misinterpretations in healthcare applications. To address this challenge, we present ECGDnet, an innovative architecture based on Transformer technology, specifically engineered to denoise multi-channel ECG signals. By leveraging multi-head self-attention mechanisms, positional embeddings, and an advanced sequence-to-sequence processing architecture, ECGDnet effectively captures both local and global temporal dependencies inherent in cardiac signals. Experimental validation on real-world datasets demonstrates ECGDnet’s remarkable efficacy in noise suppression, achieving a Signal-to-Noise Ratio (SNR) of 19.83, a Normalized Mean Squared Error (NMSE) of 0.9842, a Reconstruction Error (RE) of 0.0158, and a Pearson Correlation Coefficient (PCC) of 0.9924. These results represent significant improvements from traditional deep learning approaches while maintaining complex signal morphology and effectively mitigating noise interference. Full article

In the aging population, imbalance leading to falls is of critical concern; thus, it is imperative to determine and quantify neuromuscular changes because of rehabilitative balance training. (1) Background: Previous studies that have examined changes in balance due to rehabilitative training placed a focus on clinical measures (i.e., behavioral, kinetic, and kinematic outputs); however, irregularities due to abnormalities of underlying neural origin were unrevealed by the aforementioned measures. (2) Methods: Examining muscle activity was used to determine strategies pre- and post-six weeks of balance training in twenty-three healthy older adults (69.5 ± 5.7 years old) and five survivors of stroke (66.4 ± 9.48 years old). Surface electromyographic (sEMG) signals were recorded from eight of the lower limb muscles while participants performed forward walking (FW), forward tandem walking (FT-W), backward walking (BW), and backward tandem walking (BT-W) tasks. The sEMG data were then conditioned and muscle synergies were extracted using non-negative matrix factorization (NNMF). (3) Results: It was observed that muscle synergies and activation patterns changed for pre- versus post-balance training in older adults (i.e., healthy individuals and those who had suffered from stroke). (4) Conclusions: From our findings, it was indicative that muscle activation and muscle synergies could be used to quantify and inform rehabilitative balance training in older adults. Full article

Objective: This study aimed to investigate the effects of neuromuscular electrical stimulation (NMES) on lower limb muscle synergies during the single-leg hop test in martial artists after anterior cruciate ligament (ACL) reconstruction. Methods: Twenty-four martial artists who underwent ACL reconstruction were recruited and performed a single-leg hop test under two conditions: with NMES (ES) and without NMES (CON). The ES condition involved using Compex SP 8.0 to deliver biphasic symmetrical wave stimulation. Jump performance metrics and electromyographic (EMG) signals were recorded. Muscle synergies of the lower limbs were extracted using non-negative matrix factorization (NMF) to analyze patterns of muscle coordination. Results: Compared with the CON condition, the ES condition significantly reduced the jump time (0.13 ± 0.05 vs. 0.18 ± 0.09; F = 5.660; p = 0.022) and significantly increased the contact time (0.53 ± 0.12 vs. 0.43 ± 0.21; F = 4.013; p = 0.049). Muscle synergy analysis revealed three distinct synergy patterns under both conditions. For synergy pattern 1, compared with the CON condition, the muscle weightings of the rectus femoris and tibialis anterior muscles were significantly increased under the ES condition (p < 0.001). For synergy pattern 2, compared with the CON condition, the muscle weighting of the lateral gastrocnemius muscle was significantly increased under the ES condition (p < 0.001). Additionally, the activation timing of synergy pattern 2 was significantly reduced under the ES condition (p = 0.001). Conclusion: Neuromuscular electrical stimulation enhances jump performance and alters muscle synergy patterns in martial artists after ACL reconstruction. The findings suggest that NMES can promote better lower limb muscle coordination during jumping tasks, potentially aiding in postoperative rehabilitation and performance optimization. Full article

Background: Intraoperative nerve monitoring (IONM) is increasingly recognized as an essential technique in thyroid surgery to preserve the integrity of the recurrent laryngeal nerve (RLN) and prevent postoperative complications. Although widely adopted, several technical and anesthetic factors can significantly affect the reliability and interpretation of electromyographic (EMG) signals. Methods: This narrative review summarizes the principles and methodologies of IONM in thyroid surgery, focusing on the mechanisms of RLN injury, the clinical benefits of IONM, and its limitations. Particular emphasis is placed on the anesthesiologic considerations, including the effects of neuromuscular blocking agents and anesthetic maintenance methods for EMG signal quality. Recent advances in alternative IONM techniques are also discussed. Results: IONM facilitates early detection of RLN injury and improves surgical outcomes. However, signal loss and technical errors occur in up to 23% of cases. Appropriate anesthetic management, such as the judicious use of neuromuscular blocking agents and the use of reversal agents like sugammadex, can significantly improve IONM accuracy. Alternative approaches, such as transcutaneous or thyroid cartilage electrode-based monitoring, show promise in overcoming current limitations. Conclusions: IONM is a valuable tool in modern thyroid surgery, aiding in the prevention of RLN injury. Anesthesiologists play a crucial role in optimizing IONM quality by managing factors that affect EMG signals. Ongoing research into novel monitoring techniques is expected to further enhance patient safety and surgical precision. Full article

Background: Drug treatments for gait disorders in post-stroke patients aim to reduce muscular hyperactivity. The analysis of muscle activity is essential to help clinicians understand these disorders. This study aimed to quantify changes in muscle synergies before (PRE) and after (POST) a rectus femoris nerve block. Methods: Gait analysis of 8 post-stroke patients before and immediately after nerve block. Muscle synergies were quantified from electromyographic signals. We have selected the account for variance, which indicates the effectiveness of the synergies, the recruitment selectivity index, which indicates the degree of multiplexing of the synergies, and the recruitment consistency index, which indicates the variability of the synergy activations across gait cycles. Results: A decrease in Variance Account For (VAF) is observed, showing a lack of robustness of the underlying muscle synergies between PRE and POST. We also note that spatial and temporal primitives result in different Index of Recruitment Selectivity (IRS) but similar Index of Recruitment Consistency (IRC) for PRE vs. POST. This shows that the synergies’ activations remain consistent across cycles but are more distributed in POST than in PRE. Conclusions: The motor nerve block has not created new muscle synergies of the paretic limb during gait but indicates that there is flexibility in motor organization. This method of quantification may enable clinicians to assess the motor adaptation potential of their post-stroke patients. Full article

To enhance wrist impairment rehabilitation efficiency, self-rehabilitation training using healthy-side forearm sEMG was introduced, improving patient engagement and proprioception. A sEMG-based movement recognition and muscle force estimation algorithm was proposed to transmit the estimated results to a wrist rehabilitation robot. Dominant eigenvalues of raw forearm EMG signals were selected to construct a movement recognition model that included a BPNN, a voting decision, and an intensified algorithm. An experimental platform for muscle force estimation was established to measure sEMG under various loads. The linear fitting was performed between mean absolute values (MAVs) and external loads to derive static muscle force estimation models. A dynamic muscle force estimation model was established through linear fitting average MAVs. Volunteers wore EMG sensors and performed six typical movements to complete the verification experiment. The average accuracy of only BPNN was 90.7%, and after the addition of the voting decision and intensified algorithm, it was improved to 98.7%. In the resistance training, the measured and estimated muscle forces exhibited similar trends, with RMSE of 4.2 N for flexion/extension and 5.8 N for ulnar/radial deviation. Under two different speeds and loads, the theoretical and estimated values of dynamic muscle forces showed similar trends with almost no phase difference, and the estimation accuracy was better during flexion movements compared to radial deviations. The proposed algorithms had strong versatility and practicality, aiming to realize the self-rehabilitation trainings of patients. Full article

The global aging trend is becoming increasingly severe, and the demand for life assistance and medical rehabilitation for frail and disabled elderly people is growing. As the best solution for assisting limb movement, guiding limb rehabilitation, and enhancing limb strength, exoskeleton robots are becoming the focus of attention from all walks of life. This paper reviews the progress of research on upper limb exoskeleton robots, sEMG technology, and intention recognition technology. It analyzes the literature using keyword clustering analysis and comprehensively discusses the application of sEMG technology, deep learning methods, and machine learning methods in the process of human movement intention recognition by exoskeleton robots. It is proposed that the focus of current research is to find algorithms with strong adaptability and high classification accuracy. Finally, traditional machine learning and deep learning algorithms are discussed, and future research directions are proposed, such as using a deep learning algorithm based on multi-information fusion to fuse EEG signals, electromyographic signals, and basic reference signals. A model with stronger generalization ability is obtained after training, thereby improving the accuracy of human movement intention recognition based on sEMG technology, which provides important support for the realization of human–machine fusion-embodied intelligence of exoskeleton robots. Full article

Dysphagia is a serious complication of stroke, yet effective pharmacological treatments remain limited. This study investigated the effects of FAD012 (3,5-dimethyl-4-hydroxy cinnamic acid), a synthetic derivative of ferulic acid (FA), on cerebral damage and swallowing dysfunction in a rat model of bilateral common carotid artery occlusion (2VO). Sprague–Dawley rats were orally administered FAD012 (3 or 10 mg/kg), FA (10 mg/kg), or 0.5% carboxymethyl cellulose (CMC, suspension vehicle) starting one week before 2VO. Two weeks after 2VO surgery, which was performed under isoflurane anesthesia, reflex swallowing was assessed by electromyographic recordings of the mylohyoid muscle under urethane anesthesia. Two weeks after 2VO, cerebral blood flow (CBF) declined to approximately 40% of baseline, and the number of reflex swallowing responses was significantly reduced in the CMC group. Additionally, 2VO induced O₂⁻ production, apoptotic cell death in the striatum, and a reduction in tyrosine hydroxylase expression. Substance P (SP) levels in the laryngopharyngeal mucosa, positively regulated by dopaminergic signaling in the basal ganglia, also decreased. FAD012 (10 mg/kg) effectively prevented the 2VO-induced reduction in CBF, enhanced the reflex swallowing, and preserved the dopamine-SP system. Notably, FAD012 exerted significantly stronger effects than FA at the same dose. These findings suggest that FAD012 maintains CBF under cerebral hypoperfusion and enhances the swallowing reflex by maintaining neuronal function in the striatal and laryngopharyngeal regions of 2VO rats. Full article

Background/Objectives: Stuttering is a speech disorder involving fluency disruptions like repetitions, prolongations, and blockages, often leading to emotional distress and social withdrawal. Here, we present Augmented Multisensory Feedback Stimulation (AMFS), a novel personalized intervention to improve speech fluency in people who stutter (PWS). AMFS includes a five-day intensive phase aiming at acquiring new skills, plus a reinforcement phase designed to facilitate the transfer of these skills across different contexts and their automatization into effortless behaviors. The concept of our intervention derives from the prediction of the neurocomputational model Directions into Velocities of Articulators (DIVA). The treatment applies dynamic multisensory stimulation to disrupt PWS’ maladaptive over-reliance on sensory feedback mechanisms, promoting the emergence of participants’ natural voices. Methods: Forty-six PWS and a control group, including twenty-four non-stuttering individuals, participated in this study. Stuttering severity and physiological measures, such as heart rate and electromyographic activity, were recorded before and after the intensive phase and during the reinforcement stage in the PWS but only once in the controls. Results: The results showed a significant reduction in stuttering severity at the end of the intensive phase, which was maintained during the reinforcement training. Crucially, worse performance was found in PWS than in the controls at baseline but not after the intervention. In the PWS, physiological signals showed a reduction in activity during the training phases compared to baseline. Conclusions: Our findings show that AMFS provides a promising approach to enhancing speech fluency. Future studies should clarify the mechanisms underlying such intervention and assess whether effects persist after the treatment conclusion. Full article