Although neuromuscular decline is well documented with aging, emerging evidence indicates that it may begin as early as midlife, around age 50. As this stage represents a critical window for early intervention, the present study investigated age- and sex-related differences in muscle activation using high-density surface electromyography (HD-sEMG) of the biceps brachii (BB). Physically active individuals were categorized into three age groups: young (20-30 years), middle-aged (45-55 years), and older adults (65-75 years). HD-sEMG signals were recorded during isometric contractions at 20%, 40%, and 60% of maximal voluntary contraction (MVC). Muscle activation amplitude, spatial distribution, and signal complexity were analyzed. Although strength remained similar across groups, RMS amplitude was primarily influenced by contraction level, with age-related differences emerging in an intensity-dependent manner. In men, older participants exhibited lower RMS amplitudes compared to younger men at higher contraction levels (60% MVC, p<0.05). In women, middle-aged participants consistently exhibited lower RMS amplitudes across contraction levels, accompanied by altered spatial organization of muscle activation, reflected by higher RMS CoV and lower modified entropy at moderate-to-high contraction intensities (p<0.05). Signal complexity, assessed using sample entropy, did not show robust age-related differences, although descriptive trends toward lower values were observed in older adults at low contraction level. Taken together, these findings suggest that midlife, in women, may be characterized by subtle, task-dependent neuromuscular reorganization rather than a generalized decline. Early identification of such changes using HD-sEMG metrics may support timely interventions aimed at preserving neuromuscular function across the lifespan.

Manipulative Hand Gesture Recognition (HGR) focuses on understanding hand-object interactions and assigning semantic labels to each gesture. These techniques play a key role in applications such as robotics, virtual and augmented reality, human–computer interaction, and industrial automation. Hand gestures can be captured using diverse sensor modalities, including RGB, depth, skeleton, infrared, thermal, inertial measurement units (IMUs), surface electromyography (sEMG), and electromagnetic sensors, each with distinct advantages depending on the use case. Based on these data modalities, a wide range of models have been developed to recognize manipulative gestures using single or multiple modalities. In this study, we provide a comprehensive review of recent models in manipulative HGR, focusing on various data modalities. We first analyze the characteristics and taxonomy of manipulative gestures and review commonly used sensors and datasets. Then, we explore deep learning-based recognition methods, including single-modality approaches and multi-modality strategies such as data-level, feature-level, and score-level fusion, as well as co-learning frameworks. Lastly, we present current challenges and promising directions for future research. This study aims to present the first comprehensive review of manipulative HGR, including innovative technologies, methods, and research outcomes, and to analyze the benefits and limitations of various hand gesture recognition algorithms, aiming to contribute to future advancements in the field.

Surface electromyography (sEMG) has become a key sensing modality for hand gesture recognition in rehabilitation, prosthetic control, and human–computer interaction. However, its sparse activation patterns and sensitivity to subject- and recording-related variability pose significant challenges, particularly for fine-grained gestures involving subtle finger and wrist motions. To address these limitations, we propose an attention-enhanced multimodal fusion framework that integrates raw sEMG and accelerometer (ACC) signals. The proposed network employs narrow-kernel temporal convolutions in dedicated modality-specific streams to preserve fine-grained neuromuscular patterns, followed by a dual-attention fusion module combining multi-head self-attention (MHSA) and gated attention to model interactions between sEMG and ACC embeddings and improve gesture separability. Experiments on the NinaPro DB2 and DB7 databases show that the proposed fusion model outperforms both the sEMG-only and ACC-only models. The feature-level fusion model achieves accuracies of 96.42% on DB2 and 97.03% on DB7 without data augmentation. After applying the selected combined augmentation strategy based on time warping and scaling, the accuracy further increases to 97.56% and 98.19%, respectively. A decision-level fusion variant also achieves comparable performance while keeping the sEMG and ACC classifiers independent. Detailed gesture-level analysis further shows that the largest gains are obtained for low-amplitude and highly similar gestures, highlighting the value of multimodal fusion for improving the separability of subtle hand movements.

Surface electromyography (sEMG) is widely used for hand gesture recognition in rehabilitation and exoskeleton control. However, in this context, its sparse and variable nature often limits the recognition of fine-grained gestures. To address this, we propose a dual-stream narrow-kernel multimodal fusion framework that integrates raw sEMG and accelerometer (ACC) signals. Each modality is processed by a dedicated convolutional stream, and cross-modal dependencies are captured through multi-head self-attention and gated attention to enhance gesture discriminability. Extensive experiments on the public NinaPro DB2 and DB7 datasets, covering up to 49 gesture classes across multiple subjects, demonstrate that our approach achieves state-of-the-art performance, particularly in classifying subtle and ambiguous gestures.

As gender recognition is key in advancing personalized medicine, this study explores the use of high-density surface electromyography (HD-sEMG) signals for gender recognition during the Sit-to-Stand (STS) exercise, utilizing a combination of time, frequency, and time-frequency domain features with machine learning classifiers. A comprehensive methodology is presented, including signal preprocessing, feature extraction, and classification through conventional classifiers (K-NN, SVM, LR, DT, RF) and a hybrid CNN-KNN model, leveraging the Stockwell Transform for time-frequency image representation of the signal. Data from 64 participants across five age groups were analyzed using a 5-fold cross-validation process to ensure robustness. The CNN-KNN model achieved the highest accuracy of 99.08 % ± 1.12, significantly outperforming traditional models. Additionally, the study highlights the impact of aging on gender recognition, underscoring the importance of age-aware models for accurate predictions. This work demonstrates the potential of HD-sEMG signals for both clinical and biometric applications involving gender and age-specific analysis.

This review provides a comprehensive analysis of sEMG-IMU sensor fusion techniques for upper limb movement pattern recognition. It offers detailed insights into the signal generation mechanisms of both surface electromyography (sEMG) and inertial measurement units (IMU), and critically explores multisensory fusion strategies aimed at enhancing recognition accuracy and reliability. Key stages in the pattern recognition process, including signal acquisition, signal pre-processing, feature extraction and learning, are systematically examined. Significant advancements in tasks including hand gesture recognition (HGR), hand sign language recognition (HSLR), human activity recognition (HAR), joint angle estimation (JAE), and force/torque estimation (FE/TE) are discussed, emphasizing the role of sEMG-IMU integration in achieving improved performance. The review further explores the practical applications of these technologies in areas such as rehabilitation, prosthetic control, and human–machine interaction (HMI). Finally, this review identifies the main challenges in sEMG-IMU sensor fusion and proposed potential future research directions, focusing on overcoming current limitations and advancing the development of more robust and accurate sensor fusion models.

Amyotrophic Lateral Sclerosis (ALS) and myopathy are debilitating neuromuscular disorders that require accurate and timely diagnosis for effective management. Traditional electromyography (EMG)-based diagnostic methods rely on manual interpretation, which is time-consuming and prone to variability. This study proposes an approach that directly classifies EMG signals using a one-dimensional convolutional neural network (1D-CNN) without feature extraction, addressing the limitations of existing methods that depend on handcrafted features and focus primarily on binary classification. The proposed model is evaluated on a publicly available EMG dataset, achieving an overall accuracy of 99.27%, with macro and weighted precision, recall, and F1-scores exceeding 99% across ALS, myopathy, and healthy subjects. Unlike previous approaches that require extensive preprocessing, our method maintains high classification performance while reducing computational complexity, offering a clinically relevant multiclass classification framework. Although our method achieves high classification performance, it also maintains a strong balance between sensitivity and specificity, ensuring reliable and accurate neuromuscular disorder diagnosis, making it a practical tool for clinical applications. Future research will focus on improving model generalizability, expanding dataset diversity, and integrating real-time deployment for enhanced diagnostic utility.

High density surface electromyography (HD-sEMG) is a technique to measure the spatial electrical distribution of muscle activity, and it is increasingly applied in fundamental neuroscience, biomechanics, and exercise physiology. The HD-sEMG signals encompass a wide frequency domain with both low and high amplitudes, making them more susceptible to various types of noise. One important source of noise is power line interference (PLI) which affects the signal at the frequencies of 50Hz and its harmonics (i.e 50Hz, 100Hz, 150Hz) and can mask the real value of the signal at these frequencies that contain important information about the muscle activity. Several methods have been proposed to remove this PLI. The Canonical Correlation Analysis method has proven its efficiency in HD-sEMG signal denoising compared to other techniques but still maintained residual PLI components. In this study, we propose a new variant of the CCA denoising technique to more effectively identify and eliminate all harmonics of the PLI while preserving the HD-sEMG signal originating from other frequencies.

Introduction: Aging is associated with muscle decline, which alters both functional and anatomical properties of the neuromuscular system. These modifications can be reflected in high-density surface electromyography (HD-sEMG) signals. This study examines how age and sex impact the shape of the amplitude Probability Density Function (PDF) of HD-sEMG signals. Materials and Methods: Monopolar HD-sEMG signals were collected from the Biceps Brachii in a cohort of 17 individuals: 10 women (mean age: 22.9 f 3.6 years) and 7 men (mean age: 24.4 f 2.5 years) in the younger group, and 10 women (mean age: 69.8 f 4.8 years) and 7 men (mean age: 72.8 f 2.7 years) in the elderly group. The recordings were conducted during an elbow flexion at both 20% and 40% maximum voluntary contraction. The signal amplitude was evaluated using root means square amplitude (RMSA) and the PDF shape of each HD-sEMG signal was assessed through skewness, excess Kurtosis, and robust functional statistics. These shape distance metrics evaluate the departure from Gaussianity related to muscle aging. a) We conducted a comparison study of the HD-sEMG PDF shapes between younger and elderly individuals. b) Evaluating differences between men and women. c) Considering monopolar and Laplacian electrode configurations that are sensitive to different muscle regions. Results: A) The HD-sEMG PDFs of elderly subjects demonstrated a lower departure from Gaussianity than their younger counterparts. B) Women exhibited lower RMSA values than men, and, on average, a lower departure from Gaussianity whatever the age and contraction level C) Trends of departure from Gaussianity with contraction level, seems to be influenced by the electrode configuration. In fact, a decrease in Gaussianity departure is observed with monopolar recordings where an increase is observed with Laplacian one, clearly indicating different muscle region assessment. Discussion: The findings highlight the influence of factors such aging, sex, contraction level and electrode montage on the shape of the HD-sEMG PDF, emphasizing the significance of using this descriptor for monitoring and better assessment of muscle aging. (c) 2024 Published by Elsevier Masson SAS on behalf of AGBM.

Background: Magnetic resonance imaging (MRI) is the medical imaging technique that benefits most from recent technological innovations, particularly the constant proposal of new MRI sequences that refine clinical information from the obtained images. However, this generates new gradient-induced potential (GIP) morphologies. These induced potentials (IPs) pollute the electrophysiological signals possibly recorded simultaneously. Several algorithms developed to eliminate this noise rely on modelling the shape of the IP. As each new sequence has a different shape of IP, it might be interesting to find a mathematical approach to building sequence-specific models. In this article, we present a preliminary study that includes wavelet decomposition of contaminated electrocardiographic (ECG) to extract IP morphologies and whose time-frequency characterization allows the elaboration of a harmonic model, using sinusoidal decomposition. Method: The in vitro IPs are used to select analyzing wavelets. A broadband sensor (3.5Khz), placed inside a 3 T MRI scanner, is used to collect 3-lead ECGs while activating three sequences that generate very high noise levels. The in vivo IPs extracted from the polluted ECGs are characterized to verify their quasi-periodicity. Parameters of the sinusoidal model (amplitude, frequency, phase) are estimated using the Broyden-Fletcher-Goldfard-Shano optimization algorithm. Result: Four wavelets (sym7, coif3, bior2.2, bior3.3) showed efficient in vivo IP extraction results. Three evaluation criteria for the modelling algorithm, allowing the calculated models to be compared with the shapes of the extracted IPs, showed promising results. For example, for the chosen efficiency criterion Nash-Sutcliffe efficiency, the values obtained for the three leads are between 0.99980 and 1. Conclusion: Promising preliminary results have been obtained for the extraction on modelling of different IPs from noisy ECG signals. Continuing this preliminary study on more MRI sequences and subjects could help build a database of IP models to initiate deep learning filtering. Since these models are sequence-specific and integrate the distribution of induced voltages on the body surface, we hope to find a generic relationship that enables the prediction of IPs by new sequences and anticipate the development of purification algorithms in a near future.

Background and objectives: A reliable evaluation of anatomical and neural muscle properties and its effects on the electrical signals measured at the skin surface aims to develop a medical non-invasive aid-diagnosis tool assisted by model and personalized to the patient. This tool will be dedicated to understand and evaluate muscle diseases and aging. Methods: We perform a new Robust Morris Screening Method: RMSM in Douania et al. (2023), to assess the impact of muscle anatomy (model inputs) uncertainties and variations on a simulated HD-sEMG signals (model outputs). The model describes a complex neuromuscular system simulating HD-sEMG (high density surface electromyography) signals generated from motor units electrical sources of a striated muscle: the Biceps Brachii (BB). Two subjects categories and two contractions levels are studied: young men (YM) and old men (OM) at low and high contraction (LC = 20% of MVC and HC = 60% of MVC). A 33 features in time and frequency domains are used as model outputs. Results: We have demonstrated that the neuromuscular model is able to deliver HD-sEMG signals sensitive to the same anatomical and neural muscle factors as in real cases. Time domain features are mainly sensitive to muscle thickness, conduction velocity of fibers, electrode locations (at HC), number of motor units, and to the number of slow and fast fibers for young and aged categories respectively. Frequency domain feature are sensitive mainly to the conduction velocity of fibers and muscle conductivities (no significant differences between YM and OM are observed). Conclusion: This result is important, it allows to obtain simulated HD-sEMG signals close to experimental ones with low cost and in reduced time. However, for a reliable evaluation of muscle aging, the neuromuscular model should be enhanced to better describe structural, morphological, and functional age-related phenomena. ### Competing Interest Statement The authors have declared no competing interest.

Surface Electromyography (sEMG) has become an essential tool in various fields, including prosthetic control and clinical evaluation of the neuromusculoskeletal system. In recent years, the application of machine learning and deep learning techniques to sEMG signal classification has gained significant interest. This survey provides a detailed exploration of feature extraction methods for sEMG classification, from traditional handcrafted features to learned features. Objectives: This review aims to provide a comprehensive overview of feature extraction techniques for sEMG signal classification, focusing on both handcrafted and learned features. It seeks to advance research by offering a deeper understanding of fundamental concepts in sEMG signal analysis, along with comparisons and summaries of state-of-the-art approaches. Materials and Methods: The survey covers various feature extraction techniques used for sEMG classification, including signal acquisition, preprocessing, and the application of conventional machine learning and deep learning classifiers. It offers taxonomies, definitions, and performance comparisons, equipping researchers with a broad understanding of current methodologies. Results: Handcrafted features combined with traditional machine learning classifiers have demonstrated strong performance, especially with smaller datasets. However, deep learning techniques have shown superior results in many applications, despite challenges related to data availability and model interpretability. The survey highlights key findings regarding the performance of both approaches. Conclusion: This study bridges the gap between traditional and learned feature extraction techniques for sEMG signal classification. It provides a valuable resource for researchers and practitioners, offering insights that can guide future advancements. Key areas for future research include addressing data scarcity in deep learning and improving model interpretability for clinical applications. (c) 2024 AGBM. Published by Elsevier Masson SAS. All rights are reserved, including those for text and data mining, AI training, and similar technologies.

BioImpedance Analysis (BIA) is a safe, simple, and noninvasive technology to measure body composition. By measuring the electrical impedance of biological tissues, BIA provides valuable biological insights such as body composition, hydration status, and some health conditions. The principle is to apply an electric current to body segments, which water content and conductivity are characteristics, and to determine the electric impedance depending on body tissues passed through. However, these measurements are indirectly related to body composition and intensively depend on limited and imprecise assumptions to estimate mathematical models. This is the source of methodological and experimental challenges. BIA is very promising to offer non-invasive and portable solutions to assess health status and well-being, but challenges must be considered: they impact technological limitations, methodological standardization, and data interpretation. Advancements in BIA require to address these hurdles to improve accuracy, reliability, and applicability in diverse settings. In this article, we reviewed in depth these challenges based on a systematic review of literature. Purpose: The objective of this systematic review is to identify key challenges of BIA to assess body composition to develop possible directions for improving this technology. Our review underlines clearly the need to reduce these challenges with the multiplication of biostatistical sources, the definition of personalized models, and the adjustment of mathematical assumptions, to improve BIA reliability and adoption in e -health or specific applications. Methodology: The objective of this systematic review from published literature was to answer the question: "How to assess whole body composition in the average human adult with BIA, what are the scientific challenges and limits for a wider adoption in medical practice?". We limited our research within Pubmed, ScienceDirect and IEEE complementary databases. Our research was carried out in English using the keywords "body composition" and "bioimpedance analysis" over a period from the included 1995 to 2022. We controlled inclusion criteria to collect only articles with average human adults' groups: age from 18 years, both males and females, mixed ethnics, BMI ranging from 18 to 30 kg/m2, either healthy or non -healthy status. We added the following exclusion criteria: athletics, malnourished, eating or mental disorders, pregnancy and menstrual period. Finally, we kept articles validated versus state-ofthe-art methods DEXA, or isotope dilution. Summary findings: Our literature review identified seven major challenges with BIA: Rheological modeling precision represent human body as an electrical circuit made of resistors and capacitors to reflect electrical properties of tissues; Body compartments to model human body as a combination of cylinders different tissues type and fluids volumes; Physiological approximations as anthropometric data used in body composition modeling refer to an ancient population from 1975 (ethnicity: Caucasian, body mass index: BMI=24, sex: male, height: 170 cm, age: 25 years, health status: healthy... ); Predefined constants to predict body composition were calculated on healthy subjects; Electrical stimulation frequency choice as the impedance depends on the value and the number of frequencies used for the measure; Flow of current inside the body may not be uniform nor following the same pathway crossing all body tissues and finally Standardization of measurement protocols and body position to minimize the interferences and factors affecting the accuracy of BIA measurement. Conclusion: BIA is simple, easy to use, and noninvasive technique integrated in portable, wearable, and connected health solutions. The complexity of rheological models cannot reflect precisely the complexity of the human body. The compartment numbers considered for tissues modeling are critical for results accuracy, the commonly used configurations are the 3-C and 5-C to predict body composition referring to standard methods. Numerous physiological assumptions introduce several factors of variability that must not be generalized, the assumptions should be applied on groups with similar characteristics as the population studied only and must include subjects specificities. The models assume the use of constant values that are generic, imprecise, and estimated on limited healthy groups, future work needs to customize population -specific equations. Multiplication of electrical stimulations at different frequencies is required to consider different types of tissues and to guarantee a response from all tissues. The measures are significantly influenced by electrodes positioning, gel and dry electrodes both imply trade-offs between accuracy, convenience, and mobility. There is no one -size -fits -all answer, nevertheless standardization of procedures is a step for BIA studies to move forward and subsequently improve accuracy and reduce the gaps when results from different devices are compared. From this review, it looks critical to improve BIA methods by developing novel electrodes designs that may improve electrical contact and reduce contact impedance or by exploring the use of smart textiles and wearable electrodes for continuous monitoring of body composition and hydration status. Acquiring more data, at several electrical stimulation frequencies and in different contexts (healthy and pathological status, ethnicities, ages, comorbidities...) to enrich references and adjust constant values. Analyzing large datasets to refine prediction models. These improvements are essential prerequisites so incorporation of machine learning and artificial intelligence algorithms can explore individual variability in the future and improve the potential benefits of BIA predictions in research and clinical practice. (c) 2024 AGBM. Published by Elsevier Masson SAS. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/).

The identification of the spatial position of activated motor units (MUs) in real-time is crucial in understanding and optimizing muscle function during various activities, which contributes to advancements in various fields such as sports science, biomechanics, and neurological rehabilitation. In this study, we attempt to improve upon an existing ‘Curve Fitting Based Minimum Norm Estimation’ (CFB-MNE) approach, which uses signal processing techniques to localize MUs, with attempts to enhance its efficiency and overall capability by employing Deep Learning (DL) to solve the localization problem. The performance adequacy of several DL Convolutional Neural Network (CNN) models was explored, with their performances regarding spatial localization compared and analyzed. A dataset of high-density surface electromyography (HD-sEMG) signals, closely resembling those acquired from Biceps Brachii (BB) muscles, was generated using a multilayered volume conductor generation model. Inverse solutions were obtained then plotted as a 3D curve to finally extract the 2D images used to train the models. Testing on unseen data yielded excellent results, with all performance metrics exceeding 90%, indicating the reliability of the DL-based models in real-time spatial localization of MUs. These promising outcomes not only demonstrate the effectiveness of our current approach in accurately identifying the spatial position of individual MUs in real-time, but they also pave the way for additional improvements exploiting the abilities of more advanced DL models.

Sarcopenia is a muscle disease with adverse changes that increase throughout the lifetime but with different chronological scales between individuals. Addressing "early muscle aging" is becoming a critical issue for prevention. Through the CHRONOS study, we demonstrated the ability of the high-density surface electromyography (HD-sEMG), a noninvasive, wireless, portable technology, to detect both healthy muscle aging and accelerated muscle aging related to a sedentary lifestyle, one of the risk factors of sarcopenia. The HD-sEMG signals were analyzed in 91 healthy young, middle-aged, and old subjects (25-75 years) distributed according to their physical activity status (82 active and 9 sedentary; International Physical Activity Questionnaire) and compared with current methods for muscle evaluation, including muscle mass (dual-energy X-ray absorptiometry [DXA], ultrasonography), handgrip strength, and physical performance. The HD-sEMG signals were recorded from the rectus femoris during sit-to-stand trials, and 2 indexes were analyzed: muscular contraction intensity and muscle contraction dynamics. The clinical parameters did not differ significantly across the aging and physical activity levels. Inversely, the HD-sEMG indexes were correlated to age and were different significantly through the age categories of the 82 active subjects. They were significantly different between sedentary subjects aged 45-54 years and active ones at the same age. The HD-sEMG indexes of sedentary subjects were not significantly different from those of older active subjects (>= 55 years). The muscle thicknesses evaluated using ultrasonography were significantly different between the 5 age decades but did not show a significant difference with physical activity. The HD-sEMG technique can assess muscle aging and physical inactivity-related "early aging," outperforming clinical and DXA parameters.

This study attempts to examine the impact of age and gender on fluctuations in force output during voluntary muscle contractions and how this impacts task performance. More precisely, the study investigates how these fluctuations vary during an elbow flexion task at sub-maximal forces. Interestingly, the study found that elderly women exhibit lower steadiness, meaning they exhibit a diminished capacity to maintain a steady force output and lower complexity than young adults when dealing with high levels of contraction. This suggests that women may have a diminished ability to maintain a steady and adaptable force output during the elbow flexion task, which could affect their performance. However, the findings from this study show that the existence of notable differences in force fluctuations among diverse age groups was limited to specific levels of contraction. Thus, it can be deduced that various factors, apart from age alone, might contribute to the observed decrease in force fluctuations in this study. Additionally, there are differences in behavior between women and men.

The purpose of this preliminary study was to examine age-sensitive High Density surface Electromyogram (HD-sEMG) features by Core Shape Modelling (CSM) method. Fatiguing low force isometric contractions of the biceps brachii was performed by eight young (age, 24.40 +/- 2.42 years) and five elderly (72.90 +/- 2.21 years) males, while HD-sEMG recorded signals from the biceps brachii. The task was performed at 20 % maximal voluntary contraction (MVC). From the recorded HD-sEMG signals, three Probability Density Function (PDF) shape dis-tances (SD) measures the departure from Gaussianity, i.e. Left (LSD), Right (RSD), and Central (CSD), were derived by the CSM method from non-overlapping five-second windows until task failure. A linear regression analysis was then used to quantify the change of these shape parameters throughout the contraction. The resultant slopes revealed that the elderly group showed a decreasing trend in PDF shape parameters as the contraction approached task failure. In contrast, the young showed an increasing trend. Statistical differences between the two groups were found for LSD (p = 0.006) and RSD (p = 0.001). No such age-sensitivity was detected using conventional sEMG fatigue features. These results suggest that the proposed CSM method can be used to obtain fatigue-related features from HD-sEMG that are age-sensitive and possibly related to different motor unit recruitment and synchronization schemes.