Search Results (266)

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

A multitude of factors, including accidents, chronic illnesses, and conflicts, contribute to rising global amputation rates. The World Health Organization (WHO) estimates that 57.7 million people lived with traumatic limb amputations in 2017, with many lacking access to affordable prostheses. This study presents a preliminary framework for a low-cost, electromyography (EMG)-controlled upper limb prosthesis, integrating 3D printing and EMG sensors to enhance accessibility and functionality. Surface electrodes capture bioelectric signals from muscle contractions, processed via an Arduino Uno to actuate a one-degree-of-freedom (1-DoF) prosthetic hand. Preliminary results demonstrate reliable detection of muscle contractions (threshold = 7 ADC units, ~34 mV) and motor actuation with a response time of ~150 ms, offering a cost-effective alternative to commercial systems. While limited to basic movements, this design lays the groundwork for scalable, user-centered prosthetics. Future work will incorporate multi-DoF control, AI-driven signal processing, and wireless connectivity to improve precision and usability, advancing rehabilitation technology for amputees in resource-limited settings. Full article

The use of surface electromyography (sEMG) in equine locomotion research has increased significantly due to the essential role of balanced, symmetrical, and efficient movement in riding. However, variations in sEMG signal processing for forelimb extensor muscles across studies have made cross-study comparisons challenging. This study aims to compare the sEMG signal characteristics from carpal extensor muscles under different filtering methods: raw signal, low-pass filtering (10 Hz cut-off), and bandpass filtering (40–450 Hz cut-off and 7–200 Hz cut-off). sEMG signals were collected from four muscles of three horses during walking and trotting. The raw signals were normalized and filtered separately using a 4th-order Butterworth filter: low-pass 10 Hz, bandpass 40–450 Hz, or bandpass 7–200 Hz. For each filtered signal variant, eight activity bursts were annotated, and amplitude, root mean square (RMS), median frequency (MF), and signal-to-noise ratio (SNR) were extracted. Signal loss and residual signal were calculated to assess noise reduction and data retention. For m. extensor digitorum lateralis and m. extensor carpi ulnaris, bandpass filtering at 40–450 Hz resulted in the lowest signal loss and the highest amplitude, RMS, MF, and SNR after filtering. However, variations were observed for the other two carpal extensors. These findings support the hypotheses that the characteristics of myoelectric activity in equine carpal extensors vary depending on the filtering method applied and differ among individual muscles, thereby guiding future research on sEMG signal processing and, consequently, equine biomechanics. Since both noise and its reduction alter raw sEMG signals, potentially affecting data analysis, this study provides valuable insights for improving the reliability and reproducibility of equine biomechanics research across different sEMG studies. 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

This review examines the role of various bioelectrical signals in conjunction with artificial intelligence (AI) and analyzes how these signals are utilized in AI applications. The applications of electroencephalography (EEG), electroretinography (ERG), electromyography (EMG), electrooculography (EOG), and electrocardiography (ECG) in diagnostic and therapeutic systems are focused on. Signal processing techniques are discussed, and relevant studies that have utilized these signals in various clinical and research settings are highlighted. Advances in signal processing and classification methodologies powered by AI have significantly improved accuracy and efficiency in medical analysis. The integration of AI algorithms with bioelectrical signal processing for real-time monitoring and diagnosis, particularly in personalized medicine, is emphasized. AI-driven approaches are shown to have the potential to enhance diagnostic precision and improve patient outcomes. However, further research is needed to optimize these models for diverse clinical environments and fully exploit the interaction between bioelectrical signals and AI technologies. Full article

Background/Objectives: The manual tuning of exoskeleton control parameters is tedious and often ineffective for adapting to individual users. Human-in-the-loop (HIL) optimization offers an automated approach, but existing methods typically rely on metabolic cost, which requires prolonged data collection times of at least 60 s. Surface electromyography (EMG) signals, as an alternative, enable faster optimization with reduced data acquisition times. Methods: This study develops a rapid EMG-based HIL Bayesian optimization framework to tune hip exoskeleton controllers for assisting free leg swinging. Eight participants are asked to perform leg swinging at two frequencies with assistance from a hip exoskeleton. EMG signals from four sensors, representing muscle activity during forward and backward swings, are dynamically processed into cost functions. Bayesian optimization with Gaussian processes tunes four controller parameters using an expected improvement acquisition function. Optimization outcomes are validated against no device, zero torque, and general control baselines. Results: Optimization converges within an average of 142 s with a standard deviation of 24 s across all participants. The controller yields muscle activity reductions of 16.1% (p < 0.001) compared to no device, 21.7% (p < 0.001) versus zero torque, and 15.1% (p < 0.001) versus general control. EMG-based tuning is faster than metabolic-cost-based methods and perceived as less effortful, with Borg scale reductions of up to 39.5%. Conclusions: EMG-based HIL optimization significantly enhances controller tuning speed and effectiveness, demonstrating its potential for scalable and user-specific exoskeleton applications. Full article

Sarcopenia, affecting between 1–29% of the older population, is characterized by an age-related loss of skeletal muscle mass and function. Reduced muscle strength, either in terms of quantity or quality, and poor physical performance are among the criteria used to diagnose it. The current gold standard methods to evaluate sarcopenia are limited in terms of their cost, required expertise, and portability. A possible alternative for sarcopenia detection and monitoring is surface electromyography, which offers comprehensive information on muscle function, but a systematic synthesis of the existing literature is lacking. This systematic review aims to evaluate the application of sEMG in diagnosing and monitoring sarcopenia, focusing on the muscles involved, signal processing techniques, artificial intelligence models, and statistical analysis methods used for data interpretation. Following PRISMA guidelines, a search was performed in PubMed, Scopus, and IEEE databases from 2014 up to December 2024. Original studies using sEMG for sarcopenia diagnosis or assessment in older populations were included. After removing duplicates, 145 articles were identified, of which 18 were included in the final analysis. The findings indicate a growing interest in the adoption of sEMG in sarcopenia assessment. However, methodological heterogeneity among studies limits comparability. sEMG represents a promising option for the early detection of sarcopenia, but standardized guidelines for data collection and interpretation are needed. Future studies should focus on clinical validation and results reproducibility. Full article

The study of muscle activation patterns using surface electromyography (sEMG) provides critical insights into muscle coordination, enabling advancements in prosthetics, robotics, and rehabilitation by improving intuitive control, replicating human movements, and developing targeted therapeutic strategies. The study involved 15 healthy participants aged 20–27, using Trigno Avanti sensors to record sEMG signals from forearm muscles during specific gestures, with data processed into activation maps to analyze muscle activity and coordination for applications in rehabilitation and prosthetics. The results revealed distinct muscle activation patterns for each gesture, highlighting precise muscle coordination, with specific muscles like m. flexor carpi ulnaris and m. extensor digitorum showing varying levels of involvement depending on the movement, while m. brachioradialis remained inactive across all gestures. The study’s findings enhance our understanding of motor control by revealing specific muscle activation patterns for different hand gestures, highlighting the selectivity of muscle coordination, and suggesting avenues for future research to improve prosthetic design and rehabilitation strategies. Full article

With the rapid development of wearable technology, multifunctional sensors have demonstrated immense application potential. However, the limitations of traditional rigid materials restrict the flexibility and widespread adoption of such sensors. Hydrogels, as flexible materials, provide an effective solution to this challenge due to their excellent stretchability, biocompatibility, and adaptability. This study developed a multifunctional flexible sensor based on a composite hydrogel of polyvinyl alcohol (PVA) and sodium alginate (SA), using poly(3,4-ethylenedioxythiophene)/polystyrene sulfonate (PEDOT:PSS) as the conductive material to achieve multifunctional detection of strain, temperature, and physiological signals. The sensor features a simple fabrication process, low cost, and low impedance. Experimental results show that the prepared hydrogel exhibits outstanding mechanical properties and conductivity, with a strength of 118.8 kPa, an elongation of 334%, and a conductivity of 256 mS/m. In strain sensing, the sensor demonstrates a rapid response to minor strains (4%), high sensitivity (gauge factors of 0.39 for 0–120% and 0.73 for 120–200% strain ranges), short response time (2.2 s), low hysteresis, and excellent cyclic stability (over 500 cycles). For temperature sensing, the sensor achieves high sensitivities of −27.43 Ω/K (resistance mode) and 0.729 mV/K (voltage mode), along with stable performance across varying temperature ranges. Furthermore, the sensor has been successfully applied to monitor human motion (e.g., finger bending, wrist movement) and physiological signals such as electrocardiogram (ECG), electromyogram (EMG), and electroencephalogram (EEG), highlighting its significant potential in wearable health monitoring. By employing a simple and efficient fabrication method, this study presents a high-performance multifunctional flexible sensor, offering novel insights and technical support for the advancement of wearable devices. Full article

This article provides an overview of the implementation of electromyography (EMG) signal classification algorithms in various embedded system architectures. They address the specifications used for implementation in different devices, such as the number of movements and the type of classification method. Architectures analyzed include microcontrollers, DSP, FPGA, SoC, and neuromorphic computers/chips in terms of precision, processing time, energy consumption, and cost. This analysis highlights the capabilities of each technology for real-time wearable applications such as smart prosthetics and gesture control devices, as well as the importance of local inference in artificial intelligence models to minimize execution times and resource consumption. The results show that the choice of device depends on the required system specifications, the robustness of the model, the number of movements to be classified, and the limits of knowledge concerning design and budget. This work provides a reference for selecting technologies for developing embedded biomedical solutions based on EMG. Full article

Surface electromyography (sEMG) is increasingly important for prevention, diagnosis, and rehabilitation in healthcare. The continuous monitoring of muscle electrical activity enables the detection of abnormal events, but existing sEMG systems often rely on disposable pre-gelled electrodes that can cause skin irritation and require precise placement by trained personnel. Wearable sEMG systems integrating textile electrodes have been proposed to improve usability; however, they often suffer from poor skin–electrode coupling, leading to higher impedance, motion artifacts, and reduced signal quality. To address these limitations, we propose a preliminary model of smart socks, integrating biocompatible hybrid polymer electrodes positioned over the target muscles. Compared with commercial Ag/AgCl electrodes, these hybrid electrodes ensure lower the skin–electrode impedance, enhancing signal acquisition (19.2 ± 3.1 kΩ vs. 27.8 ± 4.5 kΩ for Ag/AgCl electrodes). Moreover, to the best of our knowledge, this is the first wearable system incorporating hydrogel-based electrodes in a sock specifically designed for the analysis of lower limb muscles, which are crucial for evaluating conditions such as sarcopenia, fall risk, and gait anomalies. The system incorporates a lightweight, wireless commercial module for data pre-processing and transmission. sEMG signals from the Gastrocnemius and Tibialis muscles were analyzed, demonstrating a strong correlation (R = 0.87) between signals acquired with the smart socks and those obtained using commercial Ag/AgCl electrodes. Future studies will further validate its long-term performance under real-world conditions and with a larger dataset. Full article

Grasping objects, from simple tasks to complex fine motor skills, is a key component of our daily activities. Our approach to facilitate the development of advanced prosthetics, robotic hands and human–machine interaction systems consists of collecting and combining surface electromyography (EMG) signals and contextual data of individuals performing manipulation tasks. In this context, the identification of patterns and prediction of hand grasp types is crucial, with cylindrical grasp being one of the most common and functional. Traditional approaches to grasp prediction often rely on unimodal data sources, limiting their ability to capture the complexity of real-world scenarios. In this work, grasp prediction models that integrate both EMG signals and contextual (task- and product-related) information have been explored to improve the prediction of cylindrical grasps during reaching movements. Three model architectures are presented: an EMG processing model based on convolutions that analyzes forearm surface EMG data, a fully connected model for processing contextual information, and a hybrid architecture combining both inputs resulting in a multimodal model. The results show that context has great predictive power. Variables such as object size and weight (product-related) were found to have a greater impact on model performance than task height (task-related). Combining EMG and product context yielded better results than using each data mode separately, confirming the importance of product context in improving EMG-based models of grasping. Full article

Gait is a complex motor process that involves the coordination and synchronization of various body parts through continuous interaction with the environment. Monitoring gait is crucial for the early detection of abnormalities, such as toe walking, which is characterized by limited or absent heel contact with the floor during walking. Persistent toe walking can cause severe foot, ankle, and musculature conditions; poor balance; increased risk of falling or tripping; and can affect overall quality of life, making it difficult, for example, to participate in sports or social activities. This study proposes a new approach to detect toe walking using surface Electromyography (sEMG) on lower limbs. sEMG sensors, by measuring the electrical activity of muscles, can see signals before the movement corresponding to muscle activation, contributing to an early detection of a possible problem. The sEMG signal presents significant complexity due to its noisy nature and the challenge of extracting meaningful features for classification. To address this issue and enhance the model’s robustness across different devices and configurations, a Transfer Learning (TL) approach is introduced. This method leverages pre-trained models to effectively handle the variability of sEMG data and improve classification accuracy. In particular, Continuous Wavelet Transform (CWT) is applied to sEMG-filtered signals (with time windows of 1 s) to convert them into 2D images (scalograms). Preliminary tests were performed on a public dataset using some of the most well-known pre-trained architectures, obtaining an accuracy of about 95% on InceptionResNetV2. Full article

This study explores two methods of predicting non-cyclic upper-body motions using classification algorithms. Exoskeletons currently face challenges with low fluency, hypothesized to be in part caused by the lag in active control innate in many leader–follower paradigms seen in today’s systems, leading to energetic inefficiencies and discomfort. To address this, we employ k-nearest neighbor (KNN) and deep learning models to predict motion characteristics, such as magnitude and category, from surface electromyography (sEMG) signals. Data were collected from six muscles located around the elbow. The sEMG signals were processed to identify significant activation changes. Two classification approaches were utilized: a KNN algorithm that categorizes motion based on the slopes of processed sEMG signals at change points and a deep neural network employing continuous categorization. Both methods demonstrated the capability to predict future voluntary non-cyclic motions up to and beyond commonly acknowledged electromechanical delay times, with the deep learning model able to predict, with certainty at or beyond 90%, motion characteristics even prior to myoelectric activation of the muscles involved. Our findings indicate that these classification algorithms can be used to predict upper-body non-cyclic motions to potentially increase machine interfacing fluency. Further exploration into regression-based prediction models could enhance the precision of these predictions, and further work could explore their effects on fluency when utilized in a tandem or wearable robotic application. Full article

Gesture recognition technology has emerged as a transformative solution for natural and intuitive human–computer interaction (HCI), offering touch-free operation across diverse fields such as healthcare, gaming, and smart home systems. In mobile contexts, where hygiene, convenience, and the ability to operate under resource constraints are critical, hand gesture recognition provides a compelling alternative to traditional touch-based interfaces. However, implementing effective gesture recognition in real-world mobile settings involves challenges such as limited computational power, varying environmental conditions, and the requirement for robust offline–online data management. In this study, we introduce ThumbsUp, which is a gesture-driven system, and employ a partially systematic literature review approach (inspired by core PRISMA guidelines) to identify the key research gaps in mobile gesture recognition. By incorporating insights from deep learning–based methods (e.g., CNNs and Transformers) while focusing on low resource consumption, we leverage Google’s MediaPipe in our framework for real-time detection of 21 hand landmarks and adaptive lighting pre-processing, enabling accurate recognition of a “thumbs-up” gesture. The system features a secure queue-based offline–cloud synchronization model, which ensures that the captured images and metadata (encrypted with AES-GCM) remain consistent and accessible even with intermittent connectivity. Experimental results under dynamic lighting, distance variations, and partially cluttered environments confirm the system’s superior low-light performance and decreased resource consumption compared to baseline camera applications. Additionally, we highlight the feasibility of extending ThumbsUp to incorporate AI-driven enhancements for abrupt lighting changes and, in the future, electromyographic (EMG) signals for users with motor impairments. Our comprehensive evaluation demonstrates that ThumbsUp maintains robust performance on typical mobile hardware, showing resilience to unstable network conditions and minimal reliance on high-end GPUs. These findings offer new perspectives for deploying gesture-based interfaces in the broader IoT ecosystem, thus paving the way toward secure, efficient, and inclusive mobile HCI solutions. Full article