Search Results (37)

Electromyography (EMG) has emerged as a vital tool in the development of wearable robotic exoskeletons, enabling intuitive and responsive control by capturing neuromuscular signals. This review presents a comprehensive analysis of the EMG signal processing pipeline tailored to exoskeleton applications, spanning signal acquisition, noise mitigation, data preprocessing, feature extraction, and control strategies. Various EMG acquisition methods, including surface, intramuscular, and high-density surface EMG, are evaluated for their applicability in real-time control. The review addresses prevalent signal quality challenges, such as motion artifacts, power-line interference, and crosstalk. It also highlights both traditional filtering techniques and advanced methods, such as wavelet transforms, empirical mode decomposition, and adaptive filtering. Feature extraction techniques are explored to support pattern recognition and motion classification. Machine learning approaches are examined for their roles in pattern recognition-based and hybrid control architectures. This article emphasizes muscle synergy analysis and adaptive control algorithms to enhance personalization and fatigue compensation, followed by the benefits of multimodal sensing and edge computing in addressing the limitations of EMG-only systems. By focusing on EMG-driven strategies through signal processing, machine learning, and sensor fusion innovations, this review bridges gaps in human–machine interaction, offering insights into improving the precision, adaptability, and robustness of next generation exoskeletons. Full article

The functional status of the plantaris muscle (PM) remains controversial and is historically dismissed as vestigial; yet, it is increasingly recognized for its structural and clinical complexity. This narrative review synthesizes current evidence from embryological development, adult morphological studies, comparative mammalian anatomy, and clinical case reports to reassess the role of the PM in humans. Developmental data reveal that the PM is consistently present during fetal life, with tendon morphology and insertion patterns emerging early and resembling adult anatomical variants. Rather than indicating postnatal regression, it suggests a stable polymorphism rooted in prenatal development. Across mammalian species, the PM varies in presence and function, correlating with locomotor specialization from proprioception in primates to propulsion in carnivores, and absence in ungulates. In humans, high proprioceptive fiber density and anatomical variability support the hypothesis that the PM may be undergoing functional repurposing from a contractile to a sensorimotor role. Clinically, its relevance is evident in imaging interpretation, surgical tendon harvesting, and the pathophysiology of Achilles tendinopathy. Recent discoveries, including the identification of the plantaris ligamentous tendon (PLT), further underscore the complexity of this region and support the need to reassess its structural and clinical significance. We conclude that the PM should not be regarded as a regressing remnant but as a dynamically adapting structure with potential neuromechanical function. Future studies involving electromyography and neuroanatomical mapping are essential to elucidate its evolving role. Full article

Upper limb assistive exoskeletons help stroke patients by assisting arm movement in impaired individuals. However, effective control of these systems to help stroke survivors is a complex task. In this paper, a novel approach is proposed to enhance the control of upper limb assistive exoskeletons by using torque estimation and prediction in a proportional–integral–derivative (PID) controller loop to more optimally integrate the torque of the exoskeleton robot, which aims to eliminate system uncertainties. First, a model for torque estimation from Electromyography (EMG) signals and a predictive torque model for the upper limb exoskeleton robot for the elbow are trained. The trained data consisted of two-dimensional high-density surface EMG (HD-sEMG) signals to record myoelectric activity from five upper limb muscles (biceps brachii, triceps brachii, anconeus, brachioradialis, and pronator teres) during voluntary isometric contractions for twelve healthy subjects performing four different isometric tasks (supination/pronation and elbow flexion/extension) for one minute each, which were trained on long short-term memory (LSTM), bidirectional LSTM (BLSTM), and gated recurrent units (GRU) deep neural network models. These models estimate and predict torque requirements. Finally, the estimated and predicted torque from the trained network is used online as input to a PID control loop and robot dynamic, which aims to control the robot optimally. The results showed that using the proposed method creates a strong and innovative approach to greater independence and rehabilitation improvement. Full article

In equine surface electromyography (sEMG), challenges related to the reliability and interpretability of data arise, among other factors, from methodological differences, including signal processing and analysis. The aim of this study is to demonstrate the filtering–induced changes in basic signal features in relation to the balance between signal loss and noise attenuation. Raw sEMG signals were collected from the quadriceps muscle of six horses during walk, trot, and canter and then filtered using eight filtering methods with varying cut–off frequencies (low–pass at 10 Hz, high–pass at 20 Hz and 40 Hz, and bandpass at 20–450 Hz, 40–450 Hz, 7–200 Hz, 15–500 Hz, and 30–500 Hz). For each signal variation, signal features—such as amplitude, root mean square (RMS), integrated electromyography (iEMG), median frequency (MF), and signal–to–noise ratio (SNR)—along with signal loss metrics and power spectral density (PSD), were calculated. High–pass filtering at 40 Hz and bandpass filtering at 40–450 Hz introduced significant filtering–induced changes in signal features while providing full attenuation of low–frequency noise contamination, with no observed differences in signal loss between these two methods. Other filtering methods led to only partial attenuation of low–frequency noise, resulting in lower signal loss and less consistent changes across gaits in signal features. Therefore, filtering–induced changes should be carefully considered when comparing signal features from studies using different filtering approaches. These findings may support cross-referencing in equine sEMG research related to training, rehabilitation programs, and the diagnosis of musculoskeletal diseases, and emphasize the importance of applying standardized filtering methods, particularly with a high–pass cut–off frequency set at 40 Hz. Full article

Amyotrophic lateral sclerosis (ALS) is a progressive disease of both upper motor neurons (UMNs) and lower motor neurons (LMNs) leading invariably to decline in motor function. The clinical exam is foundational to the diagnosis of the disease, and ordinal severity scales are used to track its progression. However, the lack of objective biomarkers of disease classification and progression delay clinical trial enrollment, muddle inclusion criteria, and limit accurate assessment of drug efficacy. Ultimately, biomarker evidence of therapeutic target engagement will support, and perhaps supplant, more traditional clinical trial outcome measures. Electrophysiology tools including nerve conduction study and electromyography (EMG) have already been established as diagnostic biomarkers of LMN degeneration in ALS. Additional understanding of the motor manifestations of disease is provided by motor unit number estimation, electrical impedance myography, and single-fiber EMG techniques. Dysfunction of UMN and non-motor brain areas is being increasingly assessed with transcranial magnetic stimulation, high-density electroencephalography, and magnetoencephalography; less common autonomic and sensory nervous system dysfunction in ALS can also be characterized. Although most of these techniques are used to explore the underlying disease mechanisms of ALS in research settings, they have the potential on a broader scale to noninvasively identify disease subtypes, predict progression rates, and assess physiologic engagement of experimental therapies. Full article

Several studies have shown interstitial cystitis/bladder pain syndrome (IC/BPS), a chronic condition that poses challenges in both diagnosis and treatment, is associated with painful pelvic floor muscles (PFM) and altered neural drive to these muscles. However, its pathophysiology could also involve other alterations in the electrical activity of PFM motor units (MUs). Studying these alterations could provide novel insights into IC/BPS and help its clinical management. This study aimed to characterize PFM activity at the MU level in women with IC/BPS and pelvic floor myalgia using high-density surface electromyography (HD-sEMG). Signals were recorded from 15 patients and 15 healthy controls and decomposed into MU action potential (MUAP) spike trains. MUAP amplitude, firing rate, and magnitude-squared coherence between spike trains were compared across groups. Results showed that MUAPs had significantly lower amplitudes during contractions on the patients’ left PFM, and delta-band coherence was significantly higher at rest on their right PFM compared to controls. These findings suggest altered PFM tissue and neuromuscular control in women with IC/BPS and pelvic floor myalgia. Our results demonstrate that HD-sEMG can provide novel insights into IC/BPS-related PFM dysfunction and biomarkers that help identify subgroups of IC/BPS patients, which may aid their diagnosis and treatment. Full article

Background: Innovative algorithms for wearable devices and garments are critical for diagnosing and monitoring disease (such as lateral epicondylitis (LE)) progression. LE affects individuals across various professions and causes daily problems. Methods: We analyzed signals from the forearm muscles of 14 healthy controls and 14 LE patients using high-density surface electromyography. We discerned significant differences between groups by employing phase–amplitude coupling (PAC) features. Our study leveraged PAC, Daubechies wavelet with four vanishing moments (db4), and state-of-the-art techniques to train a neural network for the subject’s label prediction. Results: Remarkably, PAC features achieved 100% specificity and sensitivity in predicting unseen subjects, while state-of-the-art features lagged with only 35.71% sensitivity and 28.57% specificity, and db4 with 78.57% sensitivity and 85.71 specificity. PAC significantly outperformed the state-of-the-art features (adj. p-value < 0.001) with a large effect size. However, no significant difference was found between PAC and db4 (adj. p-value = 0.147). Also, the Jeffries–Matusita (JM) distance of the PAC was significantly higher than other features (adj. p-value < 0.001), with a large effect size, suggesting PAC features as robust predictors of neuromuscular diseases, offering a profound understanding of disease pathology and new avenues for interpretation. We evaluated the generalization ability of the PAC model using 99.9% confidence intervals and Bayesian credible intervals to quantify prediction uncertainty across subjects. Both methods demonstrated high reliability, with an expected accuracy of 89% in larger, more diverse populations. Conclusions: This study’s implications might extend beyond LE, paving the way for enhanced diagnostic tools and deeper insights into the complexities of neuromuscular disorders. Full article

Electromyography-based gesture recognition has become a challenging problem in the decoding of fine hand movements. Recent research has focused on improving the accuracy of gesture recognition by increasing the complexity of network models. However, training a complex model necessitates a significant amount of data, thereby escalating both user burden and computational costs. Moreover, owing to the considerable variability of surface electromyography (sEMG) signals across different users, conventional machine learning approaches reliant on a single feature fail to meet the demand for precise gesture recognition tailored to individual users. Therefore, to solve the problems of large computational cost and poor cross-user pattern recognition performance, we propose a feature selection method that combines mutual information, principal component analysis and the Pearson correlation coefficient (MPP). This method can filter out the optimal subset of features that match a specific user while combining with an SVM classifier to accurately and efficiently recognize the user’s gesture movements. To validate the effectiveness of the above method, we designed an experiment including five gesture actions. The experimental results show that compared to the classification accuracy obtained using a single feature, we achieved an improvement of about 5% with the optimally selected feature as the input to any of the classifiers. This study provides an effective guarantee for user-specific fine hand movement decoding based on sEMG signals. Full article

The adoption of high-density electrode systems for human–machine interfaces in real-life applications has been impeded by practical and technical challenges, including noise interference, motion artefacts and the lack of compact electrode interfaces. To overcome some of these challenges, we introduce a wearable and stretchable electromyography (EMG) array, and present its design, fabrication methodology, characterisation, and comprehensive evaluation. Our proposed solution comprises dry-electrodes on flexible printed circuit board (PCB) substrates, eliminating the need for time-consuming skin preparation. The proposed fabrication method allows the manufacturing of stretchable sleeves, with consistent and standardised coverage across subjects. We thoroughly tested our developed prototype, evaluating its potential for application in both research and real-world environments. The results of our study showed that the developed stretchable array matches or outperforms traditional EMG grids and holds promise in furthering the real-world translation of high-density EMG for human–machine interfaces. Full article

Background and Objectives: Muscle strength evaluation using high-density surface electromyography (HD-sEMG) was recently developed for the detailed analysis of the motor unit (MU). Detection of the spatial distribution of sEMG can detect changes in MU recruitment patterns resulting from muscle-strengthening exercises. We conducted a prospective study in 2022 to evaluate the safety and feasibility of transcutaneous electrical sensory stimulation (TESS) therapy using an interferential current device (IFCD) in patients with head and neck squamous cell carcinoma (HNSCC) undergoing chemoradiotherapy (CRT), and reported the safety and feasibility of TESS. We evaluated the efficacy of swallowing exercises in patients with HNSCC undergoing CRT and determined the significance of sEMG in evaluating swallowing function. Materials and Methods: In this supplementary study, the patients performed muscle-strengthening exercises five days a week. The association of the effects of the exercises with body mass index, skeletal muscle mass index, HD-sEMG, tongue muscle strength, and tongue pressure were evaluated. Results: We found significant correlations between the rate of weight loss and skeletal muscle mass index reduction and the rate of change in the recruitment of the MU of the suprahyoid muscle group measured using HD-sEMG. Conclusions: We believe that nutritional supplementation is necessary in addition to muscle strengthening during CRT. Full article

Non-specific low back pain (NSLBP) is a significant and pervasive public health issue in contemporary society. Despite the widespread prevalence of NSLBP, our understanding of its underlying causes, as well as our capacity to provide effective treatments, remains limited due to the high diversity in the population that does not respond to generic treatments. Clustering the NSLBP population based on shared characteristics offers a potential solution for developing personalized interventions. However, the complexity of NSLBP and the reliance on subjective categorical data in previous attempts present challenges in achieving reliable and clinically meaningful clusters. This study aims to explore the influence and importance of objective, continuous variables related to NSLBP and how to use these variables effectively to facilitate the clustering of NSLBP patients into meaningful subgroups. Data were acquired from 46 subjects who performed six simple movement tasks (back extension, back flexion, lateral trunk flexion right, lateral trunk flexion left, trunk rotation right, and trunk rotation left) at two different speeds (maximum and preferred). High-density electromyography (HD EMG) data from the lower back region were acquired, jointly with motion capture data, using passive reflective markers on the subject’s body and clusters of markers on the subject’s spine. An exploratory analysis was conducted using a deep neural network and factor analysis. Based on selected variables, various models were trained to classify individuals as healthy or having NSLBP in order to assess the importance of different variables. The models were trained using different subsets of data, including all variables, only anthropometric data (e.g., age, BMI, height, weight, and sex), only biomechanical data (e.g., shoulder and lower back movement), only neuromuscular data (e.g., HD EMG activity), or only balance-related data. The models achieved high accuracy in categorizing individuals as healthy or having NSLBP (full model: 93.30%, anthropometric model: 94.40%, biomechanical model: 84.47%, neuromuscular model: 88.07%, and balance model: 74.73%). Factor analysis revealed that individuals with NSLBP exhibited different movement patterns to healthy individuals, characterized by slower and more rigid movements. Anthropometric variables (age, sex, and BMI) were significantly correlated with NSLBP components. In conclusion, different data types, such as body measurements, movement patterns, and neuromuscular activity, can provide valuable information for identifying individuals with NSLBP. To gain a comprehensive understanding of NSLBP, it is crucial to investigate the main domains influencing its prognosis as a cohesive unit rather than studying them in isolation. Simplifying the conditions for acquiring dynamic data is recommended to reduce data complexity, and using back flexion and trunk rotation as effective options should be further explored. Full article

High-density electromyography (HD-EMG) arrays allow for the study of muscle activity in both time and space by recording electrical potentials produced by muscle contractions. HD-EMG array measurements are susceptible to noise and artifacts and frequently contain some poor-quality channels. This paper proposes an interpolation-based method for the detection and reconstruction of poor-quality channels in HD-EMG arrays. The proposed detection method identified artificially contaminated channels of HD-EMG for signal-to-noise ratio (SNR) levels 0 dB and lower with ≥99.9% precision and ≥97.6% recall. The interpolation-based detection method had the best overall performance compared with two other rule-based methods that used the root mean square (RMS) and normalized mutual information (NMI) to detect poor-quality channels in HD-EMG data. Unlike other detection methods, the interpolation-based method evaluated channel quality in a localized context in the HD-EMG array. For a single poor-quality channel with an SNR of 0 dB, the F1 scores for the interpolation-based, RMS, and NMI methods were 99.1%, 39.7%, and 75.9%, respectively. The interpolation-based method was also the most effective detection method for identifying poor channels in samples of real HD-EMG data. F1 scores for the detection of poor-quality channels in real data for the interpolation-based, RMS, and NMI methods were 96.4%, 64.5%, and 50.0%, respectively. Following the detection of poor-quality channels, 2D spline interpolation was used to successfully reconstruct these channels. Reconstruction of known target channels had a percent residual difference (PRD) of 15.5 ± 12.1%. The proposed interpolation-based method is an effective approach for the detection and reconstruction of poor-quality channels in HD-EMG. Full article

This study investigated electromyography (EMG)–force relations using both simulated and experimental approaches. A motor neuron pool model was first implemented to simulate EMG–force signals, focusing on three different conditions that test the effects of small or large motor units located more or less superficially in the muscle. It was found that the patterns of the EMG–force relations varied significantly across the simulated conditions, quantified by the slope (b) of the log-transformed EMG-force relation. b was significantly higher for large motor units, which were preferentially located superficially rather than for random depth or deep depth conditions (p < 0.001). The log-transformed EMG–force relations in the biceps brachii muscles of nine healthy subjects were examined using a high-density surface EMG. The slope (b) distribution of the relation across the electrode array showed a spatial dependence; b in the proximal region was significantly larger than the distal region, whereas b was not different between the lateral and medial regions. The findings of this study provide evidence that the log-transformed EMG–force relations are sensitive to different motor unit spatial distributions. The slope (b) of this relation may prove to be a useful adjunct measure in the investigation of muscle or motor unit changes associated with disease, injury, or aging. Full article

Recording electrical muscle activity using a dense matrix of detection points (high-density electromyography, EMG) is of interest in a range of different applications, from human-machine interfacing to rehabilitation and clinical assessment. The wider application of high-density EMG is, however, limited as the clinical interfaces are not convenient for practical use (e.g., require conductive gel/cream). In the present study, we describe a novel dry electrode (TEX) in which the matrix of sensing pads is screen printed on textile and then coated with a soft polymer to ensure good skin-electrode contact. To benchmark the novel solution, an identical electrode was produced using state-of-the-art technology (polyethylene terephthalate with hydrogel, PET) and a process that ensured a high-quality sample. The two electrodes were then compared in terms of signal quality as well as functional application. The tests showed that the signals collected using PET and TEX were characterised by similar spectra, magnitude, spatial distribution and signal-to-noise ratio. The electrodes were used by seven healthy subjects and an amputee participant to recognise seven hand gestures, leading to similar performance during offline analysis and online control. The comprehensive assessment, therefore, demonstrated that the proposed textile interface is an attractive solution for practical applications. Full article

The anatomical territory where the neuromuscular junctions are grouped corresponds to the innervation zone (IZ). This can be located in-vivo using high-density electromyography and voluntary muscle contractions. However, in patients with motor impairment, the use of contractions imposed by electrical stimulation (ES) could be an alternative. The present study has two aims: Firstly, to describe the location of the IZ in-vivo of the medial gastrocnemius (MG) using imposed contractions by ES. Secondly, to compare the usefulness of M-waves and H-reflexes to localize the IZs. Twenty-four volunteers participated (age: 21.2 ± 1.5 years). ES was elicited in the tibial nerve to obtain M-waves and H-reflexes in the MG. The evaluators used these responses to localize the IZs relative to anatomical landmarks. M-wave and H-reflex IZ frequency identification were compared. The IZs of the MG were mostly located in the cephalocaudal direction, at 39.7% of the leg length and 34% of the knee’s condylar width. The IZs were most frequently identified in the M-wave (83.33%, 22/24) compared to the H-reflex (8.33%, 2/24) (p > 0.001). Imposed contractions revealed that the IZ of the MG is located at 39.7% of the leg length. To locate the IZs of the MG muscle, the M-wave is more useful than the H-reflex. Full article