Muscle Movement Research Articles

Describing the Flick Movement Kinematic Biomechanically and Investigating the Effect of Thrower's Ten Exercises in Underwater Hockey Players.

Analyzing flick movement kinematics biomechanically is important to prevent sport-related injuries in underwater hockey players since the aquatic-based flick movement is completely different from land-based flick movements. The study aimed to describe the flick movement kinematic biomechanically in underwater hockey players. Moreover, this study further aimed to investigate the effect of the Thrower's Ten exercises on flick movement kinematics in underwater hockey players. Descriptive laboratory study. Seventeen underwater hockey players (age: 26.2 [4.3]y; sports age: 6.2 [4.5]y) were included. First, 2 underwater cameras using motion capture video analysis MATLAB were used to biomechanically analyze the angular changes on the shoulder, elbow, wrist, and body while players were performing the flick movements. Players were then recruited to the Thrower's Ten exercise program for 6weeks. Flick movement kinematics and flick-throwing distance were recorded at baseline and 6weeks. The flick movement kinematic patterns demonstrated increased shoulder flexion (from 102.5° to 144.9°), wrist extension (from 9.5° to 10.8°), and upper-extremity rotation (from 5.7° to 56.8°) while decreased elbow extension (from 107.7° to 159.2°) from the stick met the puck until the competition of the movement. The Thrower's Ten exercises improved the elbow extension (P = .04), wrist extension (P = .01), body rotation (P < .001), and flick-throwing distance (P < .001) from baseline to 6weeks. This study describes the underwater flick kinematic technique biomechanically and interprets preliminary findings for the first time. Thus, 6weeks of Thrower's Ten exercise program provides more body muscle movements than the smaller ones during the flick movements and higher flick-throwing distance in underwater hockey players.

A Novel Battery-Supplied AFE EEG Circuit Capable of Muscle Movement Artifact Suppression

In this study, the fundamentals of electroencephalography signals, their categorization into frequency sub-bands, the circuitry used for their acquisition, and the impact of noise interference on signal acquisition are examined. Additionally, design specifications for medical-grade and research-grade EEG circuits and a comprehensive analysis of various analog front-end architectures for electroencephalograph (EEG) circuit design are presented. Three distinct selected case studies are examined in terms of comparative evaluation with generic EEG circuit design templates. Moreover, a novel one-channel battery-supplied EEG analog front-end circuit designed to address the requirements of usage protocols containing strong compound muscle movements is introduced. Furthermore, a realistic input signal generator circuit is proposed that models the human body and the electromagnetic interference from its surroundings. Experimental simulations are conducted in 50 Hz and 60 Hz electrical grid environments to evaluate the performance of the novel design. The results demonstrate the efficacy of the proposed system, particularly in terms of bandwidth, portability, Common Mode Rejection Ratio, gain, suppression of muscle movement artifacts, electrostatic discharge and leakage current protection. Conclusively, the novel design is cost-effective and suitable for both commercial and research single-channel EEG applications. It can be easily incorporated in Brain–Computer Interfaces and neurofeedback training systems.

A system for monitoring human postures, seizures, and falls from bed using radio and surface electromyography signals

In this work, a system for monitoring human postures, seizures, and falls from bed using received signal strength indicator (RSSI) and surface electromyography (sEMG) signals is studied through experiments. In this proposed system, a person who is located inside a wireless link is monitored by considering the change in measured RSSI signals as the 2.4 GHz IEEE 802.15.4 signals received at a receiver. Human motions in bed that affect RSSI levels can be captured. Thus, with this technique, it does not raise a privacy concern compared with vision-based technology. Additionally, sEMG signals associated with muscle movements from human postures are recorded from the human body’s abdominal muscles. Eight different activities, including normal and critical events, are tested and evaluated. Experimental results indicate that the proposed system could automatically monitor different human postures in real-time. RSSI and sEMG signals correlated to each posture have their own patterns. Furthermore, the relationship between human behaviors and RSSI and sEMG levels is summarized.

Facial micro-expression recognition based on motion magnification network and graph attention mechanism

Micro-expression is extensively studied due to their ability to fully reflect individuals' genuine emotions. However, accurate micro-expression recognition is a challenging task due to the subtle motion of facial muscle. Therefore, this paper introduces a Graph Attention Mechanism-based Motion Magnification Guided Micro-Expression Recognition Network (GAM-MM-MER) to amplify delicate muscle motions and focus on key facial landmarks. First, we propose a Swin Transformer-based network for micro-expression motion magnification (ST-MEMM) to enhance the subtle motions in micro-expression videos, thereby unveiling imperceptible facial muscle movements. Then, we propose a graph attention mechanism-based network for micro-expression recognition (GAM-MER), which optimizes facial key area maps and prioritizes adjacent nodes crucial for mitigating the influence of noisy neighbors, while attending to key feature information. Finally, experimental evaluations conducted on the CASME II and SAMM datasets demonstrate the high accuracy and effectiveness of the proposed network compared to state-of-the-art approaches. The results of our network exhibit significant superiority over existing methods. Furthermore, ablation studies provide compelling evidence of the robustness of our proposed network, substantiating its efficacy in micro-expression recognition.

Velocity-compensated intravoxel incoherent motion of the human calf muscle.

To determine whether intravoxel incoherent motion (IVIM) describes the blood perfusion in muscles better, assuming pseudo diffusion (Bihan Model 1) or ballistic motion (Bihan Model 2). IVIM parameters were measured in 18 healthy subjects with three different diffusion gradient time profiles (bipolar with two diffusion times and one with velocity compensation) and 17 b-values (0-600 s/mm2) at rest and after muscle activation. The diffusion coefficient, perfusion fraction, and pseudo-diffusion coefficient were estimated with a segmented fit in the gastrocnemius medialis (GM) and tibialis anterior (TA) muscles. Velocity-compensated gradients resulted in a decreased perfusion fraction (6.9% ± 1.4% vs. 4.4% ± 1.3% in the GM after activation) and pseudo-diffusion coefficient (0.069 ± 0.046 mm2/s vs. 0.014 ± 0.006 in the GM after activation) compared to the bipolar gradients with the longer diffusion encoding time. Increased diffusion coefficients, perfusion fractions, and pseudo-diffusion coefficients were observed in the GM after activation for all gradient profiles. However, the increase was significantly smaller for the velocity-compensated gradients. A diffusion time dependence was found for the pseudo-diffusion coefficient in the activated muscle. Velocity-compensated diffusion gradients significantly suppress the IVIM effect in the calf muscle, indicating that the ballistic limit is mostly reached, which is supported by the time dependence of the pseudo-diffusion coefficient.

Jellyfish-Inspired Soft Robot Driven by Pneumatic Bistable Actuators.

Soft actuators offer numerous potential applications; however, challenges persist in achieving a high driving force and fast response speed. In this work, we present the design, fabrication, and analysis of a soft pneumatic bistable actuator (PBA) mimicking jellyfish subumbrellar muscle motion for waterjet propulsion. Drawing inspiration from the jellyfish jet propulsion and the characteristics of bistable structure, we develop an elastic band stretch prebending PBA with a simple structure, low inflation cost, exceptional driving performance, and stable driving force output. Through a bionic analysis of jellyfish body structure and motion, we integrate the PBA into a jellyfish-like prototype, enabling it to achieve jet propulsion. To enhance the swimming performance, we introduce a skin-like structure for connecting the soft actuator to the jellyfish-like soft robot prototype. This skin-like structure optimizes the fluid dynamics during jet propulsion, resulting in improved efficiency and maneuverability. Our study further analyzes the swimming performance of the jellyfish-like prototype, demonstrating a swimming speed of 3.8 cm/s (0.32 body length/s, BL/s) for the tethered prototype and 4.7 cm/s (0.38 BL/s) for the untethered prototype. Moreover, we showcase the jellyfish-like prototype's notable load-bearing capacity and fast-forward swimming performance compared to other driving methods for underwater biomimetic robots. This work provides valuable insights for the development of highly agile and fast responsive soft robots that imitate the subumbrellar muscle of jellyfish for efficient water-jet propulsion, utilizing skin-like structures to enhance swimming performance.

An Algorithm for Initial Localization of Feature Waveforms Based on Differential Analysis Parameter Setting and Its Application in Clinical Electrocardiograms

In a biological signal analysis system, signals of the same type may exhibit significant variations in their feature waveforms. Biological signals are typically weak, which increases the complexity of their analysis. Furthermore, clinical biomedical signals are susceptible to various interferences from the human body itself, including muscle movements, respiration, and heartbeat. These interference factors further escalate the complexity and difficulty of signal analysis. Therefore, precise and targeted preprocessing is often required before analyzing these clinical biomedical signals to enhance the accuracy and reliability of subsequent feature extraction and classification. Here, we have established an effective and practical algorithm model that integrates preprocessing with the initial localization of target feature waveforms, achieving the following four objectives: 1. Determining the periodic positions of target feature waveforms. 2. Preserving the original amplitude and shape of target feature waveforms while eliminating negative interference. 3. Reducing or eliminating interference from other feature waveforms in the input signal. 4. Decreasing noise in the input signal, such as baseline drift, powerline interference, and muscle artifacts commonly found in biological signals. We have validated the algorithm on clinical electrocardiogram (ECG) data and the authoritative MIT-BIH open-source ECG database demonstrating its effectiveness and reliability.

Application value research of swallowing treatment device combined with swallowing rehabilitation training in the treatment of swallowing disorders after stroke.

Stroke is a common disabling disease, whether it is ischemic stroke or hemorrhagic stroke, both can result in neuronal damage, leading to various manifestations of neurological dysfunction. To explore of the application value of swallowing treatment device combined with swallowing rehabilitation training in the treatment of swallowing disorders after stroke. This study selected 86 patients with swallowing disorders after stroke admitted to our rehabilitation department from February 2022 to December 2023 as research subjects. They were divided into a control group (n = 43) and an observation group (n = 43) according to the treatment. The control group received swallowing rehabilitation training, while the observation group received swallowing treatment device in addition to the training. Both groups underwent continuous intervention for two courses of treatment. The total effective rate in the observation group (93.02%) was higher than that in the control group (76.74%) (P = 0.035). After intervention, the oral transit time, swallowing response time, pharyngeal transit time, and laryngeal closure time decreased in both groups compared to before intervention. In the observation group, the oral transit time, swallowing response time, and pharyngeal transit time were shorter than those in the control group after intervention. However, the laryngeal closure time after intervention in the observation group was compared with that in the control group (P = 0.142). After intervention, average amplitude value and duration of the genioglossus muscle group during empty swallowing and swallowing 5 mL of water are reduced compared to before intervention in both groups. After intervention, the scores of the chin-tuck swallowing exercise and the Standardized Swallowing Assessment are both reduced compared to pre-intervention levels in both groups. However, the observation group scores lower than the control group after intervention. Additionally, the Functional Oral Intake Scale scores of both groups are increased after intervention compared to pre-intervention levels, with the observation group scoring higher than the control group after intervention (P < 0.001). The cumulative incidence of complications in the observation group is 9.30%, which is lower than the 27.91% in the control group (P = 0.027). The combination of swallowing therapy equipment with swallowing rehabilitation training can improve the muscle movement level of the genioglossus muscle group, enhance swallowing function, and prevent the occurrence of swallowing-related complications after stroke.

Development of finger movement assistive gloves with pneumatic fabric actuators

We developed flexible, lightweight, and washable gloves with actuators to assist finger movements and improve ease of wearing. Performance and wearability were measured using standardized tests, triangulation of bending angles, electromyography (EMG), and grip strength. User satisfaction was measured using a survey. EMG sensors were attached to the flexor digitorum superficialis and extensor digitorum communis to capture movement data for grasping and releasing, lifting and putting down, and opening and closing an object with (a) gloves and an actuator, (b) gloves and no actuator, and (c) no gloves. The actuator-equipped glove weighed 31.4 g—lighter than in any earlier studies. In situation (a), the average EMG values for the four participants decreased, ranging from −2.06% to −44.1%, confirming the superior performance of the gloves. Survey results revealed high levels of user satisfaction. Our study offers insights into the development of rehabilitation robotic gloves that assist muscle movements and are easy to wear.

HTNet for micro-expression recognition

Facial expression is related to facial muscle contractions and different muscle movements correspond to different emotional states. For micro-expression recognition, the muscle movements are usually subtle, which has a negative impact on the performance of current facial emotion recognition algorithms. Most existing methods use self-attention mechanisms to capture relationships between tokens in a sequence, but they do not take into account the inherent spatial relationships between facial landmarks. This can result in sub-optimal performance on micro-expression recognition tasks. Therefore, learning to recognize facial muscle movements is a key challenge in the area of micro-expression recognition. In this paper, we propose a Hierarchical Transformer Network (HTNet) to identify critical areas of facial muscle movement. HTNet includes two major components: a transformer layer that leverages the local temporal features and an aggregation layer that extracts local and global semantical facial features. Specifically, HTNet divides the face into four different facial areas: left lip area, left eye area, right eye area and right lip area. The transformer layer is used to focus on representing local minor muscle movement with local self-attention in each area. The aggregation layer is used to learn the interactions between eye areas and lip areas. The experiments on four publicly available micro-expression datasets show that the proposed approach outperforms previous methods by a large margin. The codes and models are available at: https://github.com/wangzhifengharrison/HTNet.

Piezoresistive sensors based on bacterial cellulose/ZnO/PPy composite materials for human signal monitoring and sound detection

Wearable piezoresistive sensors based on flexible electronics have gained considerable attention in motion monitoring, human-machine interface, disease diagnosis and health monitoring. Research on flexible wearable sensors can be applied not only to basic sensing and human motion monitoring, but also to sound visualization techniques. In this paper, a piezoresistive sensor was prepared from bacterial cellulose, zinc oxide and polypyrrole and its sensing performance as a sound detector was investigated. The sensor has good sensing performance and can monitor human motion signals. As a sound detector, it can not only recognize different sound signals by monitoring the movement of the laryngeal muscles, but also sense air vibration to detect and recognize various natural sounds. Due to its ability to capture and present sound signals, it is able to build a visual-centred information dissemination system through visualization technology.

Enhancing Sensitivity across Scales with Highly Sensitive Hall Effect‐Based Auxetic Tactile Sensors

The research addresses the limitations inherent in conventional Hall effect‐based tactile sensors, particularly their restricted sensitivity by introducing an innovative metastructure. Through meticulous finite element analysis optimization, the Hall effect‐based auxetic tactile sensor (HEATS), featuring a rotating square plate configuration as the most effective auxetic pattern to enhance sensitivity, is developed. Experimental validation demonstrates significant sensitivity enhancements across a wide sensing range. HEATS exhibits a remarkable 20‐fold and 10‐fold improvement at tensile rates of 0.9% and 30%, respectively, compared to non‐auxetic sensors. Furthermore, comprehensive testing demonstrates HEATS’ exceptional precision in detecting various tactile stimuli, including muscle movements and joint angles. With its unparalleled accuracy and adaptability, HEATS offers vast potential applications in human–machine and human–robot interaction, where subtle tactile communication is a prerequisite.

Exploring Serotonin in Catch Contractions and Specific Dynamic Action in Postprandial Metabolism: Insights from Aquatic Models

Animal model organisms have long been instrumental in unraveling complex biological processes relevant to human physiology. The first study investigates how exercise and food intake affect metabolism, using zebrafish as our model. The second experiment dives into how neurotransmitters like dopamine and serotonin control muscle movements, particularly catch contractions, in mussels. Examining these processes provides insights into how muscle function and metabolism are regulated, potentially shedding light on human health and conservation efforts.

Ultrasensitive Wearable Pressure Sensors with Stress-Concentrated Tip-Array Design for Long-Term Bimodal Identification.

The great challenges for existing wearable pressure sensors are the degradation of sensing performance and weak interfacial adhesion owing to the low mechanical transfer efficiency and interfacial differences at the skin-sensor interface. Here, an ultrasensitive wearable pressure sensor is reported by introducing a stress-concentrated tip-array design and self-adhesive interface for improving the detection limit. A bipyramidal microstructure with various Young's moduli is designed to improve mechanical transfer efficiency from 72.6% to 98.4%. By increasing the difference in modulus, it also mechanically amplifies the sensitivity to 8.5V kPa-1 with a detection limit of 0.14Pa. The self-adhesive hydrogel is developed to strengthen the sensor-skin interface, which allows stable signals for long-term and real-time monitoring. It enables generating high signal-to-noise ratios and multifeatures when wirelessly monitoring weak pulse signals and eye muscle movements. Finally, combined with a deep learning bimodal fused network, the accuracy of fatigued driving identification is significantly increased to 95.6%.

Pembelajaran Menanam Padi untuk Melatih Motorik Halus pada Anak Usia Dini

The research aims to develop fine motor skills in only childhood trought rice planting activities at the TA Sanggar Anak Alam (SALAM) located in Nitiprayan village, kasihan,bentul, a village located on the border of the Yogyakarta district and Bantul district. With a total of 29 students. Fine motor skills, namely this movements, will coordinate the eyes and hands in a fairly good movement control which allows them to carry out precision and precision in small muscle movements. Learning activities to plant rice for kindergarten aged children’s fine motor skills aim to hone children’s motor coordination skills.

Examining ALS: reformed PCA and random forest for effective detection of ALS

ALS (Amyotrophic Lateral Sclerosis) is a fatal neurodegenerative disease of the human motor system. It is a group of progressive diseases that affects the nerve cells in the brain and spinal cord that control the muscle movement of the body hence, detection and classification of ALS at the right time is considered to be one of the vital aspects that can save the life of humans. Therefore, in various studies, different AI techniques are used for the detection of ALS, however, these methods are considered to be ineffectual in terms of identifying the disease due to the employment of ineffective algorithms. Hence, the proposed model utilizes Modified Principal Component Analysis (MPCA) and Modified Random Forest (MRF) for performing dimensionality reduction of all the potential features considered for effective classification of the ALS presence and absence of ALS causing mutation in the corresponding gene. The MPCA is adapted for capturing all the Low-Importance Data transformation. Furthermore, The MPCA is objected to performing three various approaches: Covariance Matrix Correlation, Eigen Vector- Eigenvalue decomposition, and selecting the desired principal components. This is done in aspects of implying the LI (Lower-Importance) Data Transformation. By choosing these potential components without any loss of features ensures better viability of selecting the attributes for ALS-causing gene classification. This is followed by the classification of the proposed model by using Modified RF by updating the clump detector technique. The clump detector is proceeded by clustering approach using K-means, and the data reduced by their dimension are grouped accordingly. These clustered data are analyzed either for ALS causing or devoid of causing ALS. Finally, the model’s performance is assessed using different evaluation metrics like accuracy, recall, F1 score, and precision, and the proposed model is further compared with the existing models to assess the efficacy of the proposed model.

Estimation of joint torque in dynamic activities using wearable A-mode ultrasound

The human body constantly experiences mechanical loading. However, quantifying internal loads within the musculoskeletal system remains challenging, especially during unconstrained dynamic activities. Conventional measures are constrained to laboratory settings, and existing wearable approaches lack muscle specificity or validation during dynamic movement. Here, we present a strategy for estimating corresponding joint torque from muscles with different architectures during various dynamic activities using wearable A-mode ultrasound. We first introduce a method to track changes in muscle thickness using single-element ultrasonic transducers. We then estimate elbow and knee torque with errors less than 7.6% and coefficients of determination (R2) greater than 0.92 during controlled isokinetic contractions. Finally, we demonstrate wearable joint torque estimation during dynamic real-world tasks, including weightlifting, cycling, and both treadmill and outdoor locomotion. The capability to assess joint torque during unconstrained real-world activities can provide new insights into muscle function and movement biomechanics, with potential applications in injury prevention and rehabilitation.

Safe Zones for Facial Fillers: Anatomical Study of SubSMAS Spaces in Asians.

The study "Spaces of the Face for Filler Procedures: Identification of subSMAS Spaces Based on Anatomical Study" explores the anatomy of facial spaces crucial for safe and effective filler injections. By delineating the subSMAS (sub-superficial musculoaponeurotic system) spaces, this research highlights how these virtual compartments, bordered by fat, muscles, fascia, and ligaments, facilitate independent muscle movement and reduce the risk of damaging critical structures. The thicker and more robust skin of East Asians necessitates deeper filler injections, emphasizing the significance of accurately identifying these spaces. A cadaver study with dyed gelatin validated the existence and characteristics of these subSMAS spaces, confirming their safety for filler procedures. Key spaces, such as the subgalea-frontalis, interfascial and temporalis, and prezygomatic spaces, were examined, illustrating safe zones for injections. The findings underscore the importance of anatomical knowledge for enhancing facial aesthetics while minimizing complications. This study serves as a guide for clinicians to perform precise and safe filler injections, providing a foundation for further research on the dynamic interactions of these spaces and long-term outcomes.

One-Step process of wrinkle microstructured iontronic pressure sensor with all fabric wearable electrode

The advancement of pressure sensors featuring high sensitivity, wide detection ranges, and linear response characteristics is important for advancing tactile sensing technologies for robots and human health monitoring. However, conventional preparation suffers from nonlinear signal output, non-environmental friendliness, and poor comfort, which affects the sensor’s ability to accurately detect different pressure levels, which is critical for subtle haptic feedback systems. To overcome these challenges, our research introduces a breakthrough in flexible capacitive pressure sensors by ingeniously integrating thermal stress-induced microstructured ionic gels with textile electrodes within a band-aid-like form factor. This method prevents the requirement for external templates or intricate manufacturing procedures, streamlining the production process while enhancing the sensor’s performance. The pivotal role of the ionic gel lies in its ability to form an efficient electric double layer (EDL), which adeptly transforms ion–electron interface interactions into measurable pressure-sensing outputs. Notably, this band-aid-based iontronic sensor showcases exceptional performance metrics. It exhibits an extraordinarily high sensitivity, reaching 5652.41 kPa−1, and demonstrates a broad pressure detection range (from 0-400 kPa). Additionally, the sensor responds rapidly to pressure changes, maintains robust long-term cycle stability, and boasts an impressively low detection threshold. Its potential applications, particularly in the precise monitoring of human muscle movements, open exciting new avenues for both health monitoring and the broader field of flexible electronic devices. This showcases the potential of everyday materials in advanced technological applications.

Beet curly top virus affects vector biology: the first transcriptome analysis of the beet leafhopper.

Curly top disease, caused by beet curly top virus (BCTV), is among the most serious viral diseases affecting sugar beets in western USA. The virus is exclusively transmitted by the beet leafhopper (BLH, Circulifer tenellus) in a circulative and non-propagative manner. Despite the growing knowledge on virus-vector interactions, our understanding of the molecular interactions between BCTV and BLH is hampered by limited information regarding the virus impact on the vector and the lack of genomic and transcriptomic resources for BLH. This study unveils the significant impact of BCTV on both the performance and transcriptome response of BLHs. Viruliferous BLHs had higher fecundity than non-viruliferous counterparts, which was evident by upregulation of differentially expressed transcripts (DETs) associated with development, viability and fertility of germline and embryos in viruliferous insects. Conversely, most DETs associated with muscle movement and locomotor activities were downregulated in viruliferous insects, implying potential behavioural modifications by BCTV. Additionally, a great proportion of DETs related to innate immunity and detoxification were upregulated in viruliferous insects. Viral infection also induced notable alterations in primary metabolisms, including energy metabolism, namely glucosidases, lipid digestion and transport, and protein degradation, along with other cellular functions, particularly in chromatin remodelling and DNA repair. This study represents the first comprehensive transcriptome analysis for BLH. The presented findings provide new insights into the multifaceted effects of viral infection on various biological processes in BLH, offering a foundation for future investigations into the complex virus-vector relationship and potential management strategies for curly top disease.