Search Results (110)

Yttrium–magnesium alloys are commonly employed as processing additives in magnesium alloy materials. Incorporating yttrium into magnesium alloys via Y-Mg intermediate alloys not only minimizes oxidation and burn-off loss but also simplifies operational procedures. Utilizing yttrium–magnesium alloys ensures a stable composition and reliable quality of magnesium alloy products, while contributing to reduced production costs and minimized environmental pollution. In this study, a molten salt co-reduction method was developed for the preparation Y-Mg intermediate alloys. The electrochemical co-reduction behaviors of Y(III) and Mg(II), as well as the transient states of Y-Mg intermediate alloys, were systematically investigated by transient electrochemical techniques. Results indicated that the reduction of Y(III) at the molybdenum (Mo) cathode is a reversible electrochemical process, whereas the reduction of Mg(II) is irreversible and diffusion-controlled. The diffusion coefficient of Y(III) and Mg(II) in the fluoride salt at 1000 °C were determined to be 3.98 × 10⁻⁵ cm²/s and 1.16 × 10⁻³ cm²/s, respectively. Electrochemical calculations revealed that the reduction of Y(III) involves a single-step transfer of three electrons, while Mg(II) involves a single-step transfer of two electrons. The corresponding electrode reactions are Y(III) + 3e⁻→Y and Mg(II) + 2e⁻→Mg, respectively. A Y-Mg alloy sample prepared by constant-current molten salt electrolysis primarily consists of the MgY phase with a composition of 88.38 wt% yttrium and 11.62 wt% magnesium. Full article

The present study evaluates the effectiveness of a non-invasive wearable sensor system, combining accelerometers, surface electromyography, and artificial intelligence, to objectively characterize swallowing in elderly individuals affected by Parkinson’s Disease, without clinically manifested dysphagia. A cohort of patients and healthy control subjects performed the same swallowing test protocol, including tasks with different viscosity boluses, positioning a commercial adhesive grid of High-Density surface Electromyography (HD-sEMG) electrodes on the submental muscle and a triaxial accelerometer over the thyroid cartilage. Relevant temporal and spectral features were extracted from electromyography data. Proper filtering and processing by machine learning and Principal Component Analysis allowed identification of two distinct clusters of subjects, one predominantly composed of controls with just a few patients, the other mostly crowded by patients. Excellent classification performances were achieved (accuracy = 83.3%, precision = 79.0%, recall = 90.7%, F1-score = 84.5%, Cohen’s kappa = 0.67), revealing consistent differences in muscle activation patterns among subjects, even in the absence of clinically diagnosed dysphagia. These results support the feasibility of wearable sensor-based assessment as a reliable and non-invasive tool for the early detection of subclinical swallowing dysfunction in Parkinson’s Disease. Full article

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

Despite its increasing use in veterinary rehabilitation, practical constraints—such as skin preparation and single-use electrodes—limit the wider adoption of surface electromyography (sEMG). Having conventional pre-gelled Ag/AgCl electrodes as reference, we made a pioneering comparison of the performance of reusable soft polymeric dry electrodes for recording paraspinal muscle activity in dogs during treadmill walking. Twelve clinically healthy Dachshunds from both genders were evaluated under two conditions, namely: (i) dry electrodes on untrimmed hair; and (ii) pre-gelled electrodes after trichotomy. Signals were acquired from the longissimus dorsi muscle at 1 kHz, processed with standardized filtering and rectification, and analyzed in both time and frequency domains. Dry electrodes yielded higher amplitude and Root Mean Square (RMS) values, but slightly lower power spectral density metrics when compared to pre-gelled electrodes. Nevertheless, frequency-domain results were broadly comparable between configurations. Dry electrodes reduce the preparation time, avoid hair clipping, and allow reusability without major signal degradation. While pre-gelled electrodes may still offer marginally superior stability during movement, our results suggest that soft polymeric dry electrodes present a feasible, less invasive, and more sustainable alternative for canine sEMG. These findings support further validation of dry electrodes in clinical populations, particularly for neuromuscular assessment in intervertebral disk disease. Full article

This paper proposes a novel electric wheelchair that uses the surface electromyographic signal (sEMG) signals generated by the occlusal muscles to control the wheelchair during occlusion, instead of the traditional electric wheelchair that requires users to use their hands or feet for control. In this work, the myoelectric signal controls the electric wheelchair so that users with limited mobility and paraplegia can operate the electric wheelchair using the myoelectric signal generated during clenching. This is achieved through the seamless transmission of user data and GPS paths to the cloud and is facilitated by the state-of-the-art Wi-Fi 6E communication technology. By leveraging cloud connectivity, the system can instantly relay critical information, such as the user’s location and movement patterns, ensuring a prompt emergency response. Furthermore, several standard methods exist to set up the myoelectric signal electrodes and analyze the signals. This novel electric wheelchair can change the daily activities of many users who have difficulty walking. This work is presented as a proof-of-concept feasibility study rather than a comprehensive clinical validation. Full article

The use of surface electromyography (sEMG) for conventional motion classification and prediction has had limitations due to sensor hardware differences. With the popularization of deep learning-based approaches to the application of motion prediction, this study explores the effects that different hardware sensor platforms have on the performance of a deep learning neural network trained to predict the one-degree-of-freedom (DoF) angular trajectory of a human. Two different sEMG sensor platforms were used to collect raw data from subjects conducting exercises, which was used to train a neural network designed to predict the future angular trajectory of the arm. The results show that the raw data originating from different sensor hardware with different configurations (including the communication method, data acquisition unit (DAQ) usage, electrode configuration, buffering method, preprocessing method, and experimental variables like the sampling frequency) produced bi-LSTM networks that performed similarly. This points to the hardware-agnostic nature of such deep learning networks. Full article

The primary objective of the presented study is to develop a comprehensive system for the acquisition of surface electromyographic (sEMG) data and to perform time–frequency analysis aimed at extracting discriminative features for the classification of hand gestures intended for the control of a simplified bionic hand prosthesis. The proposed system is designed to facilitate precise finger gesture execution in both prosthetic and robotic hand applications. This article outlines the methodology for multi-channel sEMG signal acquisition and processing, as well as the extraction of relevant features for gesture recognition using artificial neural networks (ANNs) and other well-established machine learning (ML) algorithms. Electromyographic signals were acquired using a prototypical LPCXpresso LPC1347 ARM Cortex M3 (NXP, Eindhoven, Holland) development board in conjunction with surface EMG sensors of the Gravity OYMotion SEN0240 type (DFRobot, Shanghai, China). Signal processing and feature extraction were carried out in the MATLAB 2024b environment, utilizing both the Fourier transform and the Hilbert–Huang transform to extract selected time–frequency characteristics of the sEMG signals. An artificial neural network (ANN) was implemented and trained within the same computational framework. The experimental protocol involved 109 healthy volunteers, each performing five predefined gestures of the right hand. The first electrode was positioned on the brachioradialis (BR) muscle, with subsequent channels arranged laterally outward from the perspective of the participant. Comprehensive analyses were conducted in the time domain, frequency domain, and time–frequency domain to evaluate signal properties and identify features relevant to gesture classification. The bionic hand prototype was fabricated using 3D printing technology with a PETG filament (Spectrum, Pęcice, Poland). Actuation of the fingers was achieved using six MG996R servo motors (TowerPro, Shenzhen, China), each with an angular range of ${180}^{\circ }$, controlled via a PCA9685 driver board (Adafruit, New York, NY, USA) connected to the main control unit. Full article

Background: Upper limb loss significantly impacts quality of life, and whereas myoelectric prostheses restore some function, conventional surface electromyography (sEMG) systems face challenges like poor signal quality, high cognitive burden, and suboptimal control. Phantom X, a novel implantable electrode-array-based system leveraging machine learning (ML), aims to overcome these limitations. This feasibility study assessed Phantom X’s performance using non-invasive surface EMG electrodes to approximate implantable system behavior. Methods: This single-arm, non-randomized study included 11 participants (9 able-bodied, 2 with transradial amputation) fitted with a 32-electrode cutaneous array around the forearm. EMG signals were processed through an ML algorithm to control a desk-mounted prosthesis. Performance was evaluated via gesture accuracy (GA), modified gesture accuracy (MGA), and classifier gesture accuracy (CGA) across 11 hand gestures in three arm postures. User satisfaction was also assessed among the two participants with transradial amputation. Results: Phantom X achieved an average GA of 89.0% ± 6.8%, MGA of 96.8% ± 2.0%, and CGA of 93.6% ± 4.1%. Gesture accuracy was the highest in the Arm Parallel posture and the lowest in the Arm Perpendicular posture. Thumbs Up had the highest accuracy (100%), while Index Point and Index Tap gestures showed lower performance (70% and 79% GA, respectively). The mean latency between EMG onset and gesture detection was 250.5 ± 145.9 ms, with 91% of gestures executed within 500 ms. The amputee participants reported high satisfaction. Conclusions: This study demonstrates Phantom X’s potential to enhance prosthesis control through multi-electrode EMG sensing and ML-based gesture decoding. The non-invasive evaluation suggests high accuracy and responsiveness, warranting further studies with the implantable system to assess long-term usability and real-world performance. Phantom X may offer a superior alternative to conventional sEMG-based control, potentially reducing cognitive burden and improving functional outcomes for upper limb amputees. Full article

Background: Surface electromyography (sEMG) enables the non-invasive assessment of muscle activity and is widely used in orthodontics for evaluating masticatory muscles. However, little is known about the dynamic changes in facial expression muscles during orthodontic treatment. This study aimed to investigate alterations in facial muscle tone during the leveling and alignment phase in adult female patients undergoing fixed appliance therapy. Methods: The study included 30 female patients aged 20–31 years who underwent sEMG assessment at four time points: before treatment initiation (T0), at the start of appliance placement (T1), three months into treatment (T2), and six months into treatment (T3). Muscle activity was recorded during four standardized facial expressions: eye closure, nasal strain, broad smile, and lip protrusion. Electrodes were placed on the orbicularis oris, orbicularis oculi, zygomaticus major, and levator labii superioris alaeque nasi muscles. A total of 1440 measurements were analyzed using Friedman and Conover-Inman tests (α = 0.05). Results: Significant changes in muscle tone were observed during treatment. During lip protrusion, the orbicularis oris and zygomaticus major showed significant increases in peak and minimum activity (p < 0.01). Eye closure was associated with altered orbicularis oris activation bilaterally at T3 (p < 0.01). Nasal strain induced significant changes in zygomaticus and levator labii muscle tone, particularly on the right side (p < 0.05). No significant changes were noted during broad smiling. Conclusions: Orthodontic leveling and alignment influence the activity of selected facial expression muscles, demonstrating a dynamic neuromuscular adaptation during treatment. These findings highlight the importance of considering soft tissue responses in orthodontic biomechanics and suggest potential implications for facial esthetics and muscle function monitoring. Full article

This study investigated the effects of lemon verbena extract (LVE) on sleep regulation using both a pentobarbital-induced sleep model and an EEG-based sleep assessment model in mice. To elucidate its potential mechanisms, mice were randomly assigned to five groups: control, positive control (diazepam, 2 mg/kg b.w.), and three LVE-treated groups receiving 40, 80, or 160 mg/kg b.w. via oral administration. In the pentobarbital-induced sleep model, mice underwent a two-week oral administration of LVE, followed by intraperitoneal pentobarbital injections. The results demonstrated that LVE significantly shortened sleep latency and prolonged sleep duration compared to the control group. Notably, adenosine A1 receptor expression, both at the mRNA and protein levels, was markedly upregulated in the brains of LVE-treated mice. Furthermore, LVE’s administration led to a significant increase in the mRNA expression of gamma-aminobutyric acid type A (GABA_A) receptor subunits (α2 and β2) in brain tissue. In the electroencephalography (EEG)/electromyogram (EMG)-based sleep model, mice underwent surgical implantation of EEG and EMG electrodes, followed by one week of LVE administration. Quantitative EEG analysis revealed that LVE treatment reduced wakefulness while significantly enhancing REM and NREM sleep’s duration, indicating its potential sleep-promoting effects. These findings suggest that LVE may serve as a promising natural sleep aid, improving both the quality and duration of sleep through the modulation of adenosine and GABAergic signaling pathways. Full article

Surface electromyography (sEMG) is a critical tool for quantifying muscle activity and inferring biomechanical function, enabling the detection of neuromuscular deficits through the analysis of electrical potential propagation. However, the inherent variability in sEMG signal amplitude, influenced by factors such as electrode placement, equipment characteristics, and individual physiology, necessitates robust normalization techniques for accurate comparative analysis. This study investigates the reliability and effectiveness of several normalization methods in the context of bicep and tricep muscle activation during dynamic and isometric exercises: maximum voluntary contraction (MVC), submaximal voluntary contraction (SMVC), remote voluntary contraction (RVC), mean, and peak normalization. We conducted a comprehensive experimental protocol involving healthy volunteers, capturing sEMG signals during controlled bicep curls, tricep extensions, and isometric contractions. The efficacy of each normalization method was evaluated based on its ability to minimize inter-subject variability and enhance signal consistency. Specifically, while SMVC, MVC, and RVC methods exhibited generally superior performance in normalizing bicep and tricep signals, the optimal method varied depending on the task and muscle, providing consistent and reliable data for biomechanical analysis. These results underscore the importance of selecting appropriate normalization techniques to improve the accuracy of sEMG-based assessments in clinical and sports biomechanics, contributing to the development of more effective rehabilitation protocols and performance enhancement strategies. Full article

Hand gesture recognition is a prominent topic in the recent literature, with surface ElectroMyoGraphy (sEMG) recognized as a key method for wearable Human–Machine Interfaces (HMIs). However, sensor placement still significantly impacts systems performance. This study addresses sensor displacement by introducing a fast and low-impact orientation correction algorithm for sEMG-based HMI armbands. The algorithm includes a calibration phase to estimate armband orientation and real-time data correction, requiring only two distinct hand gestures in terms of sEMG activation. This ensures hardware and database independence and eliminates the need for model retraining, as data correction occurs prior to classification or prediction. The algorithm was implemented in a hand gesture HMI system featuring a custom seven-channel sEMG armband with an Artificial Neural Network (ANN) capable of recognizing nine gestures. Validation demonstrated its effectiveness, achieving $93.36$% average prediction accuracy with arbitrary armband wearing orientation. The algorithm also has minimal impact on power consumption and latency, requiring just an additional 500 μW and introducing a latency increase of 408 μs. These results highlight the algorithm’s efficacy, general applicability, and efficiency, presenting it as a promising solution to the electrode-shift issue in sEMG-based HMI applications. 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

Electromyographic (EMG) sensors are essential tools for analyzing muscle activity, but traditional designs often face challenges such as motion artifacts, signal variability, and limited wearability. This study introduces a novel EMG sensor fabricated using Aerosol Jet Printing (AJP) technology that addresses these limitations with a focus on precision, flexibility, and stability. The innovative sensor design minimizes air interposition at the skin–electrode interface, thereby reducing variability and improving signal quality. AJP enables the precise deposition of conductive materials onto flexible substrates, achieving a thinner and more conformable sensor that enhances user comfort and wearability. Performance testing compared the novel sensor to commercially available alternatives, highlighting its superior impedance stability across frequencies, even under mechanical stress. Physiological validation on a human participant confirmed the sensor’s ability to accurately capture muscle activity during rest and voluntary contractions, with clear differentiation between low and high activity states. The findings highlight the sensor’s potential for diverse applications, such as clinical diagnostics, rehabilitation, and sports performance monitoring. This work establishes AJP technology as a novel approach for designing wearable EMG sensors, providing a pathway for further advancements in miniaturization, strain-insensitive designs, and real-world deployment. Future research will explore optimization for broader applications and larger populations. Full article

Freezing of gait (FOG) is a disabling yet poorly understood paroxysmal gait disorder affecting the vast majority of patients with Parkinson’s disease (PD) as they reach advanced stages of the disorder. Falling is one of the most disabling consequences of a FOG episode; it often results in injury and a future fear of falling, leading to diminished social engagement, a reduction in general fitness, loss of independence, and degradation of overall quality of life. Currently, there is no robust or reliable treatment against FOG in PD. In the absence of reliable and effective treatment for Parkinson’s disease, alleviating the consequences of FOG represents an unmet clinical need, with the first step being reliable FOG prediction. Current methods for FOG prediction and prevention cannot provide real-time readouts and are not sensitive enough to detect changes in walking patterns or balance. To fill this gap, we developed an sEMG system consisting of a soft, wearable garment (pair of shorts and two calf sleeves) embedded with screen-printed electrodes and stretchable traces capable of picking up and recording the electromyography activities from lower limb muscles. Here, we report on the testing of these garments in healthy individuals and in patients with PD FOG. The preliminary testing produced an initial time-to-onset commencement that persisted > 3 s across all patients, resulting in a nearly 3-fold drop in sEMG activity. We believe that these initial studies serve as a solid foundation for further development of smart digital textiles with integrated bio and chemical sensors that will provide AI-enabled, medically oriented data. Full article