Muscle Movement Research Articles

Cold-resistant, highly stretchable ionic conductive hydrogels for intelligent motion recognition in winter sports.

Conductive hydrogels have attracted much attention for their wide application in the field of flexible wearable sensors due to their outstanding flexibility, conductivity and sensing properties. However, the weak mechanical properties, lack of frost resistance and susceptibility to microbial contamination of traditional conductive hydrogels greatly limit their practical application. In this work, multifunctional polyvinyl alcohol (PVA)/carboxymethyl cellulose (CMC)/poly(acrylamide-co-1-vinyl-3-butylimidazolium bromide) (P(AAm-co-VBIMBr)) (PCPAV) ionic conductive hydrogels with high strength and good conductive, transparent, anti-freezing and antibacterial properties were constructed by introducing a network of chemically crosslinked AAm and VBIMBr copolymers into the base material of PVA and CMC by in situ free radical polymerization. Owing to the multiple interactions between the polymers, including covalent crosslinking, multiple hydrogen bonding interactions, and electrostatic interactions, the obtained ionic conductive hydrogels exhibit a high tensile strength (360.6 kPa), a large elongation at break (810.6%), good toughness, and fatigue resistance properties. The introduction of VBIMBr endows the PCPAV hydrogels with excellent transparency (∼92%), a high ionic conductivity (15.2 mS cm-1), antimicrobial activity and good flexibility and conductivity at sub-zero temperatures. Notably, the PCPAV hydrogels exhibit a wide strain range (0-800%), high strain sensitivity (GF = 3.75), fast response, long-term stability, and fantastic durability, which enable them to detect both large joint movements and minute muscle movements. Based on these advantages, it is believed that the PCPAV-based hydrogel sensors would have potential applications in health monitoring, human motion detection, soft robotics, ionic skins, human-machine interfaces, and other flexible electronic devices.

Management of Functional Status of a Floppy Child with Ayurveda and Panchakarma: A Case Report

ABSTRACT The floppy (hypotonic) condition often presents with abnormally lower muscle tone, resulting in the limpness of the body. Antigravity muscle movements are difficult to perform. This case had a brain lesion due to hypoxic–ischemic encephalopathy inside the cerebrum. Hypotonic cases in Ayurveda are close to Vata Vayadhi (~Diseases due to Vata dosha alleviation) with Avarana (~occlusion) due to Shiromarmaghata (~damage to the brain). This state is with symptoms such as Chestanasha (~loss of movement), Gatra vikshep sanga (~restricted body movements), and Sarveshu Skhalitam Cha Gatau Bhrusham (~fall during the walk) close to hypotonic status. In this article, a 2 and a ½-year-old male child with diagnosed hypotonic status and delayed milestones attended Kaumarbhritya outpatient department. He was not able to balance or stand with support and would lie down while sitting. He was managed according to Ayurveda guidelines for 180 days. The case was provided oral Ayurveda medications, Panchakarma therapy, and some basic exercises. The case was assessed after 60 days for the outcome of the treatment. After 180 days, a multifaceted improvement is seen. The patient started standing and walking without support, and his power grade of muscle and joints also improved. His advanced daily living activities that scored 10, before treatment improved to 70, after treatment (AT). With all-around improvement, his Gross Motor Functional Classification System score also improved from 3 to 1, AT. Magnetic resonance imaging changes were seen after a period of treatment, showing gliotic changes AT reflecting healing and neuroplastic changes. Conclusively, early intervention with Ayurveda can change the functional status in hypotonic conditions.

Design Science Research Methodology of Mobile Apps Development for Students with Cerebral Palsy Spastic and Dyskinetic

The Design Science Research Methodology (DSRM) is commonly used in developing and innovating information systems, particularly in creating artifacts such as mobile apps for education and healthcare. This study aimed to apply DSRM to create learning media for students with spastic and dyskinetic Cerebral Palsy at SLB Bina Mandiri ABCD Cirebon who have difficulty communicating due to limited arm muscle movement. The study produced two iterations of learning media artifacts: a book with picture cards using the PECS method and a mobile app with added Indonesian audio. The study focused on designing and developing the second artifact, which was more effective than the first. Future research could evaluate the second artifact in greater depth through client-initiated testing.

Self-adhesive, stretchable waterborne polyurethane-based flexible film as wearable conformal strain sensor for motion and health monitoring

Wearable tensile strain sensors are of great importance in both motion monitoring and next-generation, personalized health diagnostics. The accuracy, reliability and stability of the signals obtained from these sensors are significantly dependent on the conformal contact between the flexible sensor and the skin surface. In this study, we have developed a flexible double-layer film as a wearable tensile strain sensor by a simple solution-blending method and a layer-by-layer spraying method. D-sorbitol was incorporated into a waterborne polyurethane (WPU) emulsion to enhance film adhesion, achieving a strength of 7.91 N/m, and to disrupt hydrogen bonds between the WPU chains. This disruption facilitates more straightforward conformational changes of the chains under stress, thereby substantially enhancing the mechanical flexibility of the film. The sensing layer was subsequently constructed by spraying silver microparticles, exhibiting extremely high sensitivity (gauge factor = 103.01) over a 19.3% strain range. This sensor can effectively monitor joint motions and subtle muscle movements as tensile strain sensors.

Analysis of Multiscale Corticomuscular Coupling Networks Based on Ordinal Patterns.

The coupled analysis of corticomuscular function based on physiological electrical signals can identify differences in causal relationships between electroencephalogram (EEG) and surface electromyogram (sEMG) in different motor states. The existing methods are mainly devoted to the analysis in the same frequency band, while ignoring the cross-band coupling, which plays an active role in motion control. Considering the inherent multiscale characteristics of physiological signals, a method combining Ordinal Partition Transition Networks (OPTNs) and Multivariate Variational Modal Decomposition (MVMD) was proposed in this paper. The EEG and sEMG were firstly decomposed on a time-frequency scale using MVMD, and then the coupling strength was calculated by the OPTNs to construct a corticomuscular coupling network, which was analyzed with complex network parameters. Experimental data were obtained from a self-acquired dataset consisting of EEG and sEMG of 16 healthy subjects at different sizes of constant grip force. The results showed that the method was superior in representing changes in the causal link among multichannel signals characterized by different frequency bands and grip strength patterns. Complex information transfer between the cerebral cortex and the corresponding muscle groups during constant grip force output from the human upper limb. Furthermore, the sEMG of the flexor digitorum superficialis (FDS) in the low frequency band is the hub in the effective information transmission between the cortex and the muscle, while the importance of each frequency component in this transmission network becomes more dispersed as the grip strength grows, and the increase in coupling strength and node status is mainly in the γ band (30~60Hz). This study provides new ideas for deconstructing the mechanisms of neural control of muscle movements.

MMATrans: Muscle Movement Aware Representation Learning for Facial Expression Recognition via Transformers

MMATrans: Muscle Movement Aware Representation Learning for Facial Expression Recognition via Transformers

Methods for Clinical Monitoring of Neuromuscular Transmission in Anesthesiology - A Review.

The administration of general anesthesia is a crucial aspect of surgery. However, it can pose significant risks to patients, such as respiratory depression and prolonged neuromuscular blockade. To avoid such complications, it is essential to monitor neuromuscular transmission during anesthesia. While clinical tests have been used for decades to evaluate muscle function, they are now known to be unreliable, and relying on them increases the risk of postoperative complications. Thankfully, there are now six methods available for neuromuscular monitoring during anesthesia: mechanomyography, acceleromyography, electromyography, kinemyography, phonomyography, and compressomyography. Each of these methods differs in terms of their approach and methodology, and their importance in clinical practice varies accordingly. Mechanomyography involves measuring the mechanical response of a muscle to nerve stimulation, while acceleromyography measures the acceleration of muscle contraction. Electromyography records the electrical activity of muscles, while kinemyography tracks muscle movement. Phonomyography records the sound waves produced by contracting muscles, and compressomyography involves monitoring the pressure changes in a muscle during contraction. Overall, understanding the differences between these methods and their clinical significance is crucial for anesthesiologists. This review aims to provide an updated understanding of the current methods available for neuromuscular monitoring during anesthesia, so that anesthesiologists can make informed decisions about patient care and reduce the risk of postoperative complications.

A Generalized Cortico-Muscular-Cortical Network to Evaluate the Effects of Three-Week Brain Stimulation.

Post-stroke transcranial magnetic stimulation (TMS) has gradually become a brain intervention to assist patients in the recovery of motor function. The long lasting regulatory of TMS may involve the coupling changes between cortex and muscles. However, the effects of multi-day TMS on motor recovery after stroke is unclear. This study proposed to quantify the effects of three-week TMS on brain activity and muscles movement performance based on a generalized cortico-muscular-cortical network (gCMCN). The gCMCN-based features were further extracted and combined with the partial least squares (PLS) method to predict the Fugl-Meyer of upper extremity (FMUE) in stroke patients, thereby establishing an objective rehabilitation method that can evaluate the positive effects of continuous TMS on motor function. We found that the improvement of motor function after three-week TMS was significantly correlated with the complexity trend of information interaction between hemispheres and the intensity of corticomuscular coupling. In addition, the fitting coefficient ([Formula: see text]) for predicted and actual FMUE before and after TMS were 0.856 and 0.963, respectively, suggesting that the gCMCN-based measurement may be a promising method for evaluating the therapeutic effect of TMS. From the perspective of a novel brain-muscles network with dynamic contraction as the entry point, this work quantified TMS-induced connectivity differences while evaluating the potential efficacy of multi-day TMS. It provides a unique insight for the further application of intervention therapy in the field of brain diseases.

Synthesizing Real-Time Ultrasound Images of Muscle Based on Biomechanical Simulation and Conditional Diffusion Network.

Quantitative muscle function analysis based on the ultrasound imaging, has been used for various applications, particularly with recent development of deep learning methods. The nature of speckle noises in ultrasound images poses challenges to accurate and reliable data annotation for supervised learning algorithms. To obtain a large and reliable dataset without manual scanning and labelling, we proposed a synthesizing pipeline to provide synthetic ultrasound datasets of muscle movement with an accurate ground truth, allowing augmenting, training, and evaluating models for different tasks. Our pipeline contained biomechanical simulation using finite element method, an algorithm for reconstructing sparse fascicles, and a diffusion network for ultrasound image generation. With the adjustment of a few parameters, the proposed pipeline can generate a large dataset of real-time ultrasound images with diversity in morphology and pattern. With 3,030 ultrasound images generated, we qualitatively and quantitatively verified that the synthetic images closely matched with the in-vivo images. In addition, we applied the synthetic dataset into different tasks of muscle analysis. Compared to trained on an unaugmented dataset, model trained on synthetic one had better cross-dataset performance, which demonstrates the feasibility of synthesizing pipeline to augment model training and avoid over-fitting. The results of the regression task show potentials under the conditions that the number of datasets or the accurate label are limited. The proposed synthesizing pipeline can not only be used for muscle-related study, but for other similar study and model development, where sequential images are needed for training.

Analysis of the Structure and Working Principle of Intelligent Bionic Hand

Intelligent bionic prosthetic hand controlled by intelligent neural networks with perceptual feedback function is a hot topic of research for scientists around the world. This article analyzes the development status of electromyographic prosthetic hands both domestically and internationally over the years, and provides a detailed introduction to the structure of intelligent bionic hands. The intelligent bionic hand consists of three important components: 3D printed bionic hand bone structure, sensors, and intelligent neural network algorithm control system. Among them, the intelligent neural network algorithm control system is its core. The focus is on the working principle of an intelligent bionic hand, where sensors collect electromyography and neural electrical signals generated by human muscle movements. Through the intelligent neural network algorithm control system, the data information obtained is continuously optimized and adjusted to achieve motion control of the prosthetic hand. This article analyzes some problems of intelligent bionic hands and provides development ideas and suggestions. It is pointed out that the reconstruction of the sensing function of the intelligent bionic hand should focus on multimodal feedback, using diverse stimuli and more sensitive sensors for human-machine interaction, so that the intelligent bionic hand can move flexibly and freely, benefiting most disabled patients.

Restless Sleep Disorder as a New Primary Sleep Disorder in Children and Adolescents

Restless sleep is a frequent complaint clinically and often associated with specific sleep or medical conditions. Recently, restless sleep disorder (RSD) has been identified as a new pediatric sleep disorder with core features of restless sleep and frequent large muscle movements during sleep, which are not explained by other conditions. Based on the medical literature and expert clinical experience, the task force developed a consensus diagnostic criteria for RSD and scoring large muscle group movements in children aged 6–18 years. Eight essential criteria of RSD include a complaint of restless sleep, observed large body movements during sleep, video-polysomnographic documentation of ≥5 large body movements/hour, occurrence at least thrice weekly for at least 3 months, clinically significant impairment, and differentiation from other conditions that may secondarily cause restless sleep. There is evidence to support a pathophysiology based on iron deficiency, sleep instability, and increased sympathetic activation, and treatment options include oral or intravenous iron supplementation. This review describes the key features of RSD, potential pathophysiology, and current treatment options to help diagnose patients with RSD, which has been overlooked.

VaBTFER: An Effective Variant Binary Transformer for Facial Expression Recognition.

Existing Transformer-based models have achieved impressive success in facial expression recognition (FER) by modeling the long-range relationships among facial muscle movements. However, the size of pure Transformer-based models tends to be in the million-parameter level, which poses a challenge for deploying these models. Moreover, the lack of inductive bias in Transformer usually leads to the difficulty of training from scratch on limited FER datasets. To address these problems, we propose an effective and lightweight variant Transformer for FER called VaTFER. In VaTFER, we firstly construct action unit (AU) tokens by utilizing action unit-based regions and their histogram of oriented gradient (HOG) features. Then, we present a novel spatial-channel feature relevance Transformer (SCFRT) module, which incorporates multilayer channel reduction self-attention (MLCRSA) and a dynamic learnable information extraction (DLIE) mechanism. MLCRSA is utilized to model long-range dependencies among all tokens and decrease the number of parameters. DLIE's goal is to alleviate the lack of inductive bias and improve the learning ability of the model. Furthermore, we use an excitation module to replace the vanilla multilayer perception (MLP) for accurate prediction. To further reduce computing and memory resources, we introduce a binary quantization mechanism, formulating a novel lightweight Transformer model called variant binary Transformer for FER (VaBTFER). We conduct extensive experiments on several commonly used facial expression datasets, and the results attest to the effectiveness of our methods.

Implantable Epigallocatechin Gallate Sustained-Release Nanofibers for the Prevention of Immobilization-Induced Muscle Atrophy.

Long-term immobilization of joints can lead to disuse atrophy of the muscles in the joints. Oral nutrients are used clinically for rehabilitation and therapeutic purposes, but bioavailability and targeting are limited. Here, we report tea polyphenols (dietary polyphenols), sustained-release nanofilms that release tea polyphenols through slow local degradation of core-shell nanofibers in muscles. This dietary polyphenol does not require gastrointestinal consumption and multiple doses and can directly remove inflammatory factors and superoxide generated in muscle tissue during joint fixation. The quality of muscles is increased by 30%, and muscle movement function is effectively improved. Although nanofibers need to be implanted into muscles, they can improve bacterial infections after joint surgery. To investigate the biological mechanism of this core-shell nanomembrane prevention, we conducted further transcriptomic studies on muscle, confirming that in addition to achieving antioxidation and anti-inflammation by inhibiting TNF-α and NF-κB signaling pathways, tea polyphenol core-shell nanofibers can also promote muscle formation by activating the p-Akt signaling pathway.

Implementation of an Intelligent EMG Signal Classifier Using Open-Source Hardware

This paper presents the implementation of an intelligent real-time single-channel electromyography (EMG) signal classifier based on open-source hardware. The article shows the experimental design, analysis, and implementation of a solution to identify four muscle movements from the forearm (extension, pronation, supination, and flexion), for future applications in transradial active prostheses. An EMG signal acquisition instrument was developed, with a 20–450 Hz bandwidth and 2 kHz sampling rate. The signals were stored in a Database, as a multidimensional array, using a desktop application. Numerical and graphic analysis approaches for discriminative capacity were proposed for feature analysis and four feature sets were used to feed the classifier. Artificial Neural Networks (ANN) were implemented for time-domain EMG pattern recognition (PR). The system obtained a classification accuracy of 98.44% and response times per signal of 8.522 ms. Results suggest these methods allow us to understand, intuitively, the behavior of user information.

I Am an Earphone and I Can Hear My User’s Face: Facial Landmark Tracking Using Smart Earphones

This article presents EARFace , a system that shows the feasibility of tracking facial landmarks for 3D facial reconstruction using in-ear acoustic sensors embedded within smart earphones. This enables a number of applications in the areas of facial expression tracking, user interfaces, AR/VR applications, affective computing, and accessibility, among others. Although conventional vision-based solutions break down under poor lighting and occlusions, and also suffer from privacy concerns, earphone platforms are robust to ambient conditions while being privacy-preserving. In contrast to prior work on earable platforms that perform outer-ear sensing for facial motion tracking, EARFace shows the feasibility of completely in-ear sensing with a natural earphone form factor, thus enhancing the comfort levels of wearing. The core intuition exploited by EARFace is that the shape of the ear canal changes due to the movement of facial muscles during facial motion. EARFace tracks the changes in shape of the ear canal by measuring ultrasonic channel frequency response of the inner ear, ultimately resulting in tracking of the facial motion. A transformer-based machine learning model is designed to exploit spectral and temporal relationships in the ultrasonic channel frequency response data to predict the facial landmarks of the user with an accuracy of 1.83 mm. Using these predicted landmarks, a 3D graphical model of the face that replicates the precise facial motion of the user is then reconstructed. Domain adaptation is further performed by adapting the weights of layers using a group-wise and differential learning rate. This decreases the training overhead in EARFace . The transformer-based machine learning model runs on smart phone devices with a processing latency of 13 ms and an overall low power consumption profile. Finally, usability studies indicate higher levels of comforts of wearing EARFace ’s earphone platform in comparison with alternative form factors.

OVERVIEW OF MULTIPLE SCLEROSIS WITH GUT EPSILON TOXICITY IN HUMAN: SISTEMATIC LITERATURE REVIEW

Background: Multiple Sclerosis (MS) is a complex autoimmune neurodegenerative disorder that afflicts a significant number of individuals worldwide. This formidable disease, which is often life-threatening, imposes a lifelong impact on those diagnosed, altering their existence in profound ways. Methods: The current review is meticulously crafted, drawing upon a multitude of scholarly articles previously disseminated in both national and international academic journals within the same discipline. Results: This disease inflicts substantial damage to the Central Nervous System (CNS), predominantly affecting the myelin sheath of nerve fibers, spinal cord, and eyes, thereby leading to the development of muscle weakness, movement disorders, and visual impairments. Recent observations have unveiled a potential link between a gut bacterium, Clostridium perfringens, and the onset of MS in humans. Conclusion: Given that the precise etiology of Multiple Sclerosis (MS) remains elusive and presents a formidable challenge, this paper endeavors to shed light on the current landscape of the disease. It provides an in-depth exploration of its epidemiology, clinical manifestations, diagnostic procedures, prognosis, and treatment modalities, all underpinned by the most recent advancements in the fields of immunology and medical microbiology about infectious diseases.

Nusinersen induces detectable changes in compound motor action potential response in spinal muscular atrophy type 1 patients with severe impairment of motor function

BackgroundMost long-term affected spinal muscular atrophy (SMA) type 1 patients have severe impairment of motor function and are dependent on mechanical ventilation with tracheostomy. The efficacy and safety of nusinersen in these patients have not been established. MethodsWe retrospectively evaluated the efficacy of intrathecal nusinersen treatment in patients with SMA type 1 who continued treatment for at least 12 months. There were three patients enrolled in our study (3, 4 and 16 years of age) who had severe impairment of gross motor function without head control or the ability to roll over. All three needed mechanical ventilation with tracheostomy and tube feeding. Motor function was assessed using the Children s Hospital of Philadelphia infant test of neuromuscular disorders (CHOP-INTEND) and the caregivers’ evaluations. Concurrently, we examined nerve conduction longitudinally and compared compound motor action potential (CMAP) amplitudes. ResultsAll patients continued nusinersen administration without significant adverse events for more than three years. While CHOP-INTEND scores did not remarkably increase, according to the caregivers, all three patients had improved finger or facial muscle movements that enabled them to make their intentions understood. Some CMAPs before treatment were not identified but became traces after nusinersen administration. ConclusionsThe improvement in motor function that leads to smoother communication could be a basis for continuing nusinersen treatment. Currently available motor function scorings are not efficient for assessing therapeutic interventions in SMA patients with medical care complexity. Longitudinal nerve conduction studies could be an objective indicator.

Dodecanogram (DDG): Advancing EEG technology with a high- frequency brain activity measurement device

EEG measures electric potential changes in the scalp. Even though it has been associated with human thoughts, there has been no direct evidence. The problem with EEG is that it measures variations in current or electric potential in the millisecond time domain, where muscle movement strongly affects the readings. The millisecond time domain is equivalent to the kHz resonance signal generated by any dielectric resonator, and every single cell membrane resonates in this time range. So, the measurement of EEG could come simply from the skin and not from the brain. Therefore, we have advanced EEG technology with the dodecanogram (DDG), which reveals 12 frequency bands or 12 discrete time regions where brain activities are most significant. We measure brain activity using a stream of pulses and a logic analyzer that counts ultra-short pulses needed to emulate brain scalp potential changes. We have created another version of DDG where, using an array of RLC (resistor-inductor-capacitor) resonators, we sense the ultra-low-power electromagnetic radiation from different locations on the brain's surface. Since we measure signals from Hz to THz, covering 12 orders of time ranges as a property of dielectric resonance, unlike EEG, there is a high probability that the DDG signal may truly originate from the brain. We have monitored DDG on an artificial organic brain replica 24/7 for over a year and on multiple human subjects, before and after meditation and concluded that most cognitive, perceptive, and emotional bursts occur around 200-700 nanoseconds, not milliseconds, as it was believed for 150 years of EEG era.

The Inter-Relation of Objective Assessment of The Bell’s Palsy with House-Brackmann Score: A Prospective Study

Introduction: Bell’s palsy is the most common acute peripheral mononeuropathy, which leads to the partial or complete inability to voluntary movement of the facial muscles on the affected side of the face. The study aimed to evaluate the objective assessment of the Bell’s palsy with House-Brackmann score. Methods: This Observational, Prospective study was to be conducted at tertiary care hospital among from June 2022 to September 2022 on 11 patients diagnosed with Bell’s Palsy. A patient’s history includes age, sex, and clinical features. Assessments include facial nerve grading system such as House-Brackmann score, which was used to grade the facial nerve severity. Results: Out of 11 patients, 7 patients of Bell’s Palsy (63%) were reported with Grade IV of House- Brackmann score. The remaining patients were scored under Grade III and Grade V. Patients of age group from 9 to 75 were taken. Among them, age group of 9-40 years were 45% and 40- 80 years were 55%. The result shows that Grade IV is predominant over others, male and elder patients are leading over female and middle- aged patients. 63.7% patient’s clinical features were come under Grade IV of House- Brackmann score. 27.2% of patients had an increased risk of hypertension, 18.1% had Diabetes Mellitus, 18.1% had past history of CVA, 9% had past history of Seizure, and known history of Bell ’s palsy. Conclusion: Using the House-Brackmann grading system, the severity of the patient presenting with Bell’s palsy could be reliably predicted.

Catching a smile from individuals and crowds: Evidence for distinct emotional contagion processes.

Research examining how crowd emotions impact observers usually requires participants to engage in an atypical mental process whereby (static) arrays of individuals are cognitively integrated to represent a crowd. The present work sought to extend our understanding of how crowd emotions may spread to individuals by assessing self-reported emotions, attention and muscle movement in response to emotions of dynamic, virtually modeled crowd stimuli. Self-reported emotions and attention from thirty-six participants were assessed when foreground and background crowd characters exhibited homogeneous (Study 1) or heterogeneous (Study 2) positive, neutral, or negative emotions. Results suggested that affective responses in observers are shaped by crowd emotions even in the absence of direct attention. Thirty-four participants supplied self-report and facial electromyography responses to the same homogeneous (Study 3) or heterogeneous (Study 4) crowd stimuli. Results indicated that positive crowd emotions appeared to exert greater attentional pull and objective responses, while negative crowd emotions also elicited affective responses. Study 5 (n = 67) introduced a control condition (stimuli containing an individual person) to examine if responses are unique to crowds and found that emotional contagion from crowds was more intense than from individuals. These studies present methodological advances in the study of crowd emotional contagion and have implications for our broader understanding of how people process, attend, and affectively respond to crowds. Advancing theory by suggesting that emotional contagion from crowds is distinct from that elicited by individuals, findings may have applications for refining crowd management approaches. (PsycInfo Database Record (c) 2024 APA, all rights reserved).