Monitor Muscle Activity Research Articles

A single-transducer echomyography system for monitoring muscle activity

A single-transducer echomyography system for monitoring muscle activity

Behavioral changes of transgenic mice carrying Adrb1-A187V mutation with short sleep duration under different dietary conditions

To observe the effects of restricted and high-fat diets on behavioral changes of wild-type (Adrb1+/+) and transgenic mice carrying Adrb1-A187V mutation (Adrb1+/m) with short sleep durations. Adrb1+/+ and Adrb1+/m C57BL/6 mice were randomized into normal chow group (25 Adrb1+/+ and 26 Adrb1+/m mice for behavioral monitoring), odor retention fasting group (17 Adrb1+/+ and 19 Adrb1+/m mice for behavioral monitoring; 6 Adrb1+/+ mice and 6 Adrb1+/m mice for EEG/EMG monitoring), absolute fasting group (6 Adrb1+/+ and 4-5 Adrb1+/m mice for behavioral monitoring; 6 Adrb1+/+ and 6 Adrb1+/m mice for EEG/EMG monitoring), and high-fat diet group (6 Adrb1+/+ and 7 Adrb1+/m mice for behavioral monitoring; 6 Adrb1+/+ and 6 Adrb1+/m mice for EEG/EMG monitoring). Electrodes for EEG and muscle activity monitoring were implanted on the skulls of the mice. After 24 h of odor retention fasting, absolute fasting, or high-fat feeding, the mice were observed for behavioral changes adapted to diet changes. In odor retention fasting experiment, Adrb1+/m mice exhibited more stable fluctuations of activities with mildly reduced movement and prolonged sleep duration, indicating enhanced starvation resistance. In absolute fasting experiment, Adrb1+/m mice showed significantly increased nighttime water intake, improved rhythmicity in water intake (frequent intakes in small amounts), and increased duration of non-rapid eye movement sleep (NREM). In the high-fat diet experiment, Adrb1+/m mice showed higher levels of activity with increased instances of nighttime rearing, longer movement distances, and increased rapid eye movement sleep during daytime. Adrb1+/m mice can quickly respond to environmental changes and under restricted dietary conditions, they can conserve energy by increasing sleep to maintain energy homeostasis but show higher levels of activity under high-fat dietary conditions.

Ultrasound-Compatible Electrode for Functional Electrical Stimulation.

Functional electrical stimulation (FES) is a vital method in neurorehabilitation used to reanimate paralyzed muscles, enhance the size and strength of atrophied muscles, and reduce spasticity. FES often leads to increased muscle fatigue, necessitating careful monitoring of the patient's response. Ultrasound (US) imaging has been utilized to provide valuable insights into FES-induced fatigue by assessing changes in muscle thickness, stiffness, and strain. Current commercial FES electrodes lack sufficient US transparency, hindering the observation of muscle activity beneath the skin where the electrodes are placed. US-compatible electrodes are essential for accurate imaging and optimal FES performance, especially given the spatial constraints of conventional US probes and the need to monitor muscle areas directly beneath the electrodes. This study introduces specially designed body-conforming US-compatible FES (US-FES) electrodes constructed with a silver nanowire/polydimethylsiloxane (AgNW/PDMS) composite. We compared the performance of our body-conforming US-FES electrode with a commercial hydrogel electrode. The findings revealed that our US-FES electrode exhibited comparable conductivity and performance to the commercial one. Furthermore, US compatibility was investigated through phantom and in vivo tests, showing significant compatibility even during FES, unlike the commercial electrode. The results indicated that US-FES electrodes hold significant promise for the real-time monitoring of muscle activity during FES in clinical rehabilitative applications.

Contraction assessment of abdominal muscles using automated segmentation designed for wearable ultrasound applications

PurposeWearable ultrasound devices can be used to continuously monitor muscle activity. One possible application is to provide real-time feedback during physiotherapy, to show a patient whether an exercise is performed correctly. Algorithms which automatically analyze the data can be of importance to overcome the need for manual assessment and annotations and speed up evaluations especially when considering real-time video sequences. They even could be used to present feedback in an understandable manner to patients in a home-use scenario. The following work investigates three deep learning based segmentation approaches for abdominal muscles in ultrasound videos during a segmental stabilizing exercise. The segmentations are used to automatically classify the contraction state of the muscles.MethodsThe first approach employs a simple 2D network, while the remaining two integrate the time information from the videos either via additional tracking or directly into the network architecture. The contraction state is determined by comparing measures such as muscle thickness and center of mass between rest and exercise. A retrospective analysis is conducted but also a real-time scenario is simulated, where classification is performed during exercise.ResultsUsing the proposed segmentation algorithms, 71% of the muscle states are classified correctly in the retrospective analysis in comparison to 90% accuracy with manual reference segmentation. For the real-time approach the majority of given feedback during exercise is correct when the retrospective analysis had come to the correct result, too.ConclusionBoth retrospective and real-time analysis prove to be feasible. While no substantial differences between the algorithms were observed regarding classification, the networks incorporating the time information showed temporally more consistent segmentations. Limitations of the approaches as well as reasons for failing cases in segmentation, classification and real-time assessment are discussed and requirements regarding image quality and hardware design are derived.

Electrical Impedance Tomography for Real-Time Monitoring of Muscle Activity in Robot-Assisted Rehabilitation

This work looks at how Electrical Impedance Tomography (EIT) can be used in robot-assisted rehabilitation to make it easier to watch how muscle activity changes in real time. Quantitative methods are used in this study to look into how well EIT works, including full analyses and comparisons with common tracking methods like Electromyography (EMG). Both EIT and EMG are used to record the participants' mean and peak muscle activation levels as they do a set of Upper Limb, Lower Limb, and Full Body exercises. The study's findings show that EIT is incredibly flexible when it comes to capturing complex patterns of muscle activation across a wide range of rehabilitation routines. Each participant's involvement is shown by different patterns of muscle activation, and these patterns can be seen across different types of exercise. We can learn a lot about how muscles work during rehabilitation by measuring their mean and peak activation levels. This helps us see how well EIT works as a tracking tool. A very important step in validating EIT is to compare it to EMG readings. This shows how consistent and accurate EIT is in real-time monitoring situations. The high level of agreement between the two methods shows that EIT is a reliable alternative to standard monitoring methods, providing an easy to use and non-invasive way to measure muscle activity. The results of this study are even stronger because they include quantitative analyses and a detailed look at system performance data. We carefully check things like signal-to-noise ratio, reconstruction accuracy, and reliability, which shows how strong and dependable the combined EIT system is. These metrics are important for showing how well the system can measure things accurately and reliably in changing rehabilitation situations. This study adds to the growing amount of research on EIT uses in rehabilitation by showing that it can be a useful tool for keeping track of real-time muscle activity. This study opens the door for EIT to be widely used in rehabilitation by showing how muscle activation changes over time and proving its effectiveness against well-known methods. This will eventually lead to better patient outcomes and more effective rehabilitation.

Monitoring muscle activity in pediatric SCI: Insights from sensorized rocking chairs and machine-learning.

Introduction: Activity-based therapy is effective at improving trunk control in children with spinal cord injury. A prototype sensorized rocking chair was developed and confirmed as an activity that activates trunk muscles. This study uses data collected from the chair to predict muscle use during rocking. Methods: The prototype rocking chair included sensors to detect forces, accelerations, as well child and chair movement. Children with spinal cord injury and typically developing children (2-12years), recruited under an approved IRB protocol, were observed rocking while sensor and electromyography data were collected from arm, leg, and trunk muscles. Features from sensor data were used to predict muscle activation using multiple linear regression, regression learning, and neural network modeling. Correlation analysis examined individual sensor contributions to predictions. Results: Neural network models outperformed regression models. Multiple linear regression predictions significantly correlated (p < 0.05) with targets for four of eleven children with SCI, while decision tree regression predictions correlated for five children. Neural network predictions correlated for all children. Conclusions: Embedded sensors capture useful information about muscle activation, and machine learning techniques can be used to inform therapists. Further work is warranted to refine prediction models and to investigate how well results can be generalized.

Signal Processing for Spinal Cord Injury Monitoring with sEMG

Spinal cord injuries (SCIs) significantly impair motor and sensory functions, posing challenges to rehabilitation and patient management. Accurate monitoring of SCIs is crucial for evaluating the extent of injury and guiding therapeutic interventions. This study explores the application of surface electromyography (sEMG) as a non-invasive tool for monitoring muscle activity in individuals with SCIs. By capturing electrical signals generated by muscle fibers, sEMG provides real-time data essential for assessing neuromuscular function. To enhance the effectiveness of sEMG in spinal cord injury monitoring, advanced signal processing techniques are employed. These techniques include noise reduction, which improves signal clarity, feature extraction to identify key patterns in muscle activation, and pattern recognition algorithms to classify muscle responses. The integration of these methods enables more accurate interpretations of sEMG data, facilitating a better understanding of motor capabilities and rehabilitation progress. The findings highlight the potential of using signal processing in conjunction with sEMG to improve patient outcomes, optimize rehabilitation protocols, and provide insights into the underlying mechanisms of spinal cord injuries. This approach not only advances clinical practices but also supports ongoing research aimed at enhancing the quality of life for individuals affected by SCIs.

Fabrication and Evaluation of Carrageenan Based Bioplastic with Graphite and Ag-Nanoparticles Addition as Flexible Electrode for EMG Signal Measurement

Electromyography (EMG) is a method for measuring muscle biopotential signals for monitoring muscle activity. Electrodes are placed on the skin to capture EMG signals from muscles underneath. The most common electrodes used in clinical EMG measurement are Ag/AgCl electrodes in the form of metal plates coated with electrode gel. Electrode gel enhances the contact between the electrode’s metal plate and the skin since it is essential for a good measurement signal quality. Meanwhile, flexible electrodes are made from flexible conductive materials that can be adjusted to the contour of the skin surface; therefore, they can improve the measured biopotential signal quality. This study developed a carrageenan-based bioplastic with the addition of graphite and silver nanoparticles (AgNP) hybrid as a flexible electrode for EMG signal measurement. Fabrication of graphite and AgNP hybrid starts with the functionalization of the graphite powder in a mixture of HNO3 and H2SO4. Next, AgNPs were added using the electrochemical method by utilizing SnCl2 and functionalized graphite powder to form an Ag-Sn/Graphite (Graphite-AgNPs) hybrid conductive material. In order to incorporate conductive materials into bioplastic, the Graphite-AgNPs hybrid conductive material is then mixed into the carrageenan-based bioplastic mixture. It is found that 25% w/w addition of these conductive materials already gives good electrical conductivity. The best electrical conductivity value was determined by varying several conductive material types and concentrations. Finally, the EMG signal was measured with the bioplastic flexible electrodes, and the performance was compared with the commercial Ag/AgCl electrodes.

Development of a Low-Cost Portable EMG for Measuring the Muscular Activity of Workers in the Field.

This study explores the development and validation of a low-cost electromyography (EMG) device for monitoring muscle activity and muscle fatigue by monitoring the key features in EMG time and frequency domains. The device consists of a Raspberry Pico microcontroller interfacing a Myoware EMG module. The experiment involved 34 volunteers (14 women, 20 men) who performed isometric and isotonic contractions using a hand dynamometer. The low-cost EMG device was compared to a research-grade EMG device, recording EMG signals simultaneously. Key features including root mean square (RMS), median power frequency (MDF), and mean power frequency (MNF) were extracted to evaluate muscle fatigue. During isometric contraction, a strong congruence between the two devices, with similar readings and behavior of the extracted features, was observed, and the Wilcoxon signed rank test confirmed no significant difference in the ability to detect muscle fatigue between the devices. For isotonic contractions, the low-cost device demonstrated behavior similar to the professional EMG device in 70.58% of cases, despite some susceptibility to noise and movement. This suggests the potential viability of the low-cost EMG device as a portable tool for assessing muscle fatigue, enabling accessible and cost-effective management of muscle health in various work scenarios.

Experimental Tests of Effect of Vertical Lifting Height, Horizontal Distance, and Weight Load Lifting Method on Muscle Activation Value Generated Using Electromyography

In industry, manual lifting activities certainly involve muscles and generate risks if done improperly. There are problems in the form of complaints from industrial workers on manual lifting activities that are carried out in a less ergonomic manner. This study aims to determine the effect of lifting factor variables on the resulting level of muscle activation. In this case, three factors are used, namely the lifting method factor, the horizontal distance factor, and the vertical distance factor, each of which has two factor levels to produce eight combinations of lifting factors. Randomization was carried out on research subjects totalling 10 people. Previous research that has been done manually is by calculating the pulse resulting from a lifting activity according to a specified frequency. In this study, monitoring of muscle activity was carried out automatically using an instrument called Electromyography (EMG) at three points of the hand muscles, namely the biceps brachii muscle, triceps brachii muscle, and brachioradialis muscle. The results of this study indicate the best combination based on the value of muscle activation on the biceps brachii muscle, triceps brachii muscle, and brachioradialis muscle which respectively include combination 2 (M1-H2-V1) with a significance value of 0.002, combination 6 (M2-H2 -V1) with a significance value of 0.000 and combination 2 (M1-H2-V1) with a significance value of 0.013. The resulting combination of the best lifting factors can be used as a reference and recommendations for the ideal lifting posture and conditions for workers in minimizing possible injuries

A Multi-Day Wearable Surface EMG E-Tattoo for Fatigue Monitoring.

Surface electromyography (sEMG) is a commonly used technique for the non-invasive measurement of muscle activity. However, the traditional electrodes used for sEMG often have limitations regarding their long-term wearability. This study explored the feasibility of a wearable platform using a tattoo-like epidermal electrode (e-tattoo) for multi-day sEMG monitoring. Our sEMG e-tattoo provided stable and reliable sEMG signals over three days of application comparable to conventional gel electrodes. In addition, the e-tattoo has great resistance to motion artifacts and, therefore, maintains a high signal-to-noise ratio (SNR) and signal-to-motion ratio (SMR) during dynamic activities such as cycling. This robust wearable platform opens up new avenues for developing future wearable sEMG devices and advancing dynamic muscle fatigue research.Clinical relevance- The proposed wearable sEMG system can provide continuous and non-invasive monitoring of muscle activity, providing insights for improving rehabilitation and EMG-based prosthesis development for patients.

Wearables for personalized monitoring of masticatory muscle activity - opportunities, challenges, and the future.

Wearable devices are worn on or remain in close proximity of the human body. The use of wearable devices specific to the orofacial region is steadily increasing. Orofacial applications of wearable devices include supplementing diagnosis, tracking treatment progress, monitoring patient compliance, and understanding oral parafunctional behaviours. In this short communication, the role of wearable devices in advancing personalized dental medicine are highlighted with a specific focus on masticatory muscle activity monitoring in naturalistic settings. Additionally, challenges, opportunities, as well as future research areas for successful use of wearable devices for precise, personalized care of muscle disorders are discussed.

Human Arm Workout Classification by Arm Sleeve Device Based on Machine Learning Algorithms.

Wearables have been applied in the field of fitness in recent years to monitor human muscles by recording electromyographic (EMG) signals. Understanding muscle activation during exercise routines allows strength athletes to achieve the best results. Hydrogels, which are widely used as wet electrodes in the fitness field, are not an option for wearable devices due to their characteristics of being disposable and skin-adhesion. Therefore, a lot of research has been conducted on the development of dry electrodes that can replace hydrogels. In this study, to make it wearable, neoprene was impregnated with high-purity SWCNTs to develop a dry electrode with less noise than hydrogel. Due to the impact of COVID-19, the demand for workouts to improve muscle strength, such as home gyms and personal trainers (PT), has increased. Although there are many studies related to aerobic exercise, there is a lack of wearable devices that can assist in improving muscle strength. This pilot study proposed the development of a wearable device in the form of an arm sleeve that can monitor muscle activity by recording EMG signals of the arm using nine textile-based sensors. In addition, some machine learning models were used to classify three arm target movements such as wrist curl, biceps curl, and dumbbell kickback from the EMG signals recorded by fiber-based sensors. The results obtained show that the EMG signal recorded by the proposed electrode contains less noise compared to that collected by the wet electrode. This was also evidenced by the high accuracy of the classification model used to classify the three arms workouts. This work classification device is an essential step towards wearable devices that can replace next-generation PT.

Highly Sensitive and Selective Real-Time Breath Isoprene Detection using the Gas Reforming Reaction of MOF-Derived Nanoreactors

Real-time breath isoprene sensing provides noninvasive methods for monitoring human metabolism and early diagnosis of cardiovascular diseases. Nonetheless, the stable alkene structure and high humidity of the breath hinder sensitive and selective isoprene detection. In this work, we derived well-defined Co3O4@polyoxometalate yolk-shell structures using a metal-organic framework template. The inner space, including highly catalytic Co3O4 yolks surrounded by a semipermeable polyoxometalate shell, enables stable isoprene to be reformed to reactive intermediate species by increasing the gas residence time and the reaction with the inner catalyst. This sensor exhibited selective isoprene detection with an extremely high chemiresistive response (180.6) and low detection limit (0.58 ppb). The high sensing performance can be attributed to electronic sensitization and catalytic promotion effects. In addition, the reforming reaction of isoprene is further confirmed by the proton transfer reaction-quadrupole mass spectrometry analysis. The practical feasibility of this sensor in smart healthcare applications is exhibited by monitoring muscle activity during the workout.

Development of a Wearable Ultrasound Transducer for Sensing Muscle Activities in Assistive Robotics Applications

Robotic prostheses and powered exoskeletons are novel assistive robotic devices for modern medicine. Muscle activity sensing plays an important role in controlling assistive robotics devices. Most devices measure the surface electromyography (sEMG) signal for myoelectric control. However, sEMG is an integrated signal from muscle activities. It is difficult to sense muscle movements in specific small regions, particularly at different depths. Alternatively, traditional ultrasound imaging has recently been proposed to monitor muscle activity due to its ability to directly visualize superficial and at-depth muscles. Despite their advantages, traditional ultrasound probes lack wearability. In this paper, a wearable ultrasound (US) transducer, based on lead zirconate titanate (PZT) and a polyimide substrate, was developed for a muscle activity sensing demonstration. The fabricated PZT-5A elements were arranged into a 4 × 4 array and then packaged in polydimethylsiloxane (PDMS). In vitro porcine tissue experiments were carried out by generating the muscle activities artificially, and the muscle movements were detected by the proposed wearable US transducer via muscle movement imaging. Experimental results showed that all 16 elements had very similar acoustic behaviors: the averaged central frequency, -6 dB bandwidth, and electrical impedance in water were 10.59 MHz, 37.69%, and 78.41 Ω, respectively. The in vitro study successfully demonstrated the capability of monitoring local muscle activity using the prototyped wearable transducer. The findings indicate that ultrasonic sensing may be an alternative to standardize myoelectric control for assistive robotics applications.

Development of a Wireless Multichannel Near-Infrared Spectroscopy Sensor System for Monitoring Muscle Activity

Near-infrared spectroscopy (NIRS) technology is a noninvasive technology that can detect the hemoglobin concentration in the shallow tissue of humans in the form of an optical signal and determine the muscle’s activation state based on the change of hemoglobin concentration. It has a broad application prospect in the biomedical and physiological signal analysis. There are specific requirements for the structure of NIRS sensors for different application scenarios. The muscle synergy theory explains that body movements are often accompanied by the cooperation of many different muscles. Therefore, a multichannel NIRS system is necessary for motion recognition. This article presents a distributed multichannel NIRS system. The wireless communication avoids the interference of signal lines, and the equipped upper computer system can display NIRS signals in real time. Our NIRS sensors have three different wavelengths of near-infrared light (750, 800, and 850 nm) and two photodiodes, which can effectively improve the sampling accuracy of the signal. Experimental verification and evaluation prove that our system’s performance and reliability are better than those of other NIRS systems. Finally, cuff occlusion and hand motion experiments demonstrated that blood flow and oxygen consumption are the two main factors that cause hemoglobin concentration changes. We offer a new scheme for NIRS signal analysis that uses the common mode and difference mode of Hb and HbO2, and the results demonstrate its superior performance. In future work, we will discuss the application and effect of the NIRS system in rehabilitation training.

Robot-assisted assessment of the effects of self-aligning mechanism on the wrist passive range of motion

Axis alignment between human and robot has been a research hotspot in wearable rehabilitation robots and has a significant effects on full natural ranges of motion (ROM) during robot-assisted training exercises. Self-alignment mechanism (SAM) has been extensively applied on the rehabilitation robots to adaptively compensate the axis misalignment between human anatomic and robot mechanical joints. However, the effects of the SAM on the wrist ROM have not been definitely assessed. In this study, we implemented a SAM on a wrist rehabilitation device (WReD II), and the SAM was specially designed to be locked or unlocked to achieve self-alignment function or not according to demands. To assess the effects of SAM on the wrist passive ROM of extension/flexion (E/F), passive training experiments involving 8 healthy participants were conducted by locking or unlocking the SAM respectively. Surface Electromyography (sEMG) was applied to monitor muscle activity during experiments. During the experiments, the wrist passive ROM were measured by applying the robot-assisted assessment technique. Preliminary results show that the means of the passive ROM have a maximum increase of 19.78° in the case with SAM unlocked when compared to the case with SAM locked. Statistical results indicated high test-retest reliability of the passive ROM measurements with intraclass correlation coefficient ICC (2,1) more than 0.82, and standard error of measurement (SEM) less than 3.2°. These findings suggest that the implement of SAM on the rehabilitation robots has great potentials due to its feasibility and reliability to obtain larger natural passive ROM.

Comparison of different algorithms based on TKEO for EMG change point detection

Objective. A significant challenge in surface electromyography (EMG) is the accurate identification of onset and offset of muscle activation while maintaining high real-time performance. Teager–Kaiser energy operator (TKEO) is widely used in muscle activity monitoring systems because of its computational simplicity and strong real-time performance. However, in contrast to TKEO ontology, few studies have examined how well the energy operator variants from multiple fields perform in conditioning EMG signals. This paper aims to investigate the role of the energy operator and its variants in EMG change point detection by a threshold detector. Approach. To compare the stability and accuracy of TKEO and its variants for EMG change point detection, the EMG data of extensor carpi radialis longus and flexor carpi radialis were acquired from twenty participants operating a controller under normal and disturbed conditions, and EMG change point detection was performed by four energy operators and their rectified versions. Main results. Based on the ‘standard’ change points collected by the controller, the detection results were evaluated by three evaluation indexes: detection rate, F1 Score, and accuracy. The experimental results show that the multiresolution energy operator and the TKEO with rectified (abs-TKEO) are more suitable for EMG change point detection. Significance. This paper compared the effect of the energy operator and its variants on a threshold-based EMG change point detector. The experimental results in this paper can provide a reference for the selection of EMG signal conditioning methods to improve the detection performance of the EMG change point detector.

Towards Soft Wearable Strain Sensors for Muscle Activity Monitoring

The force-generating capacity of skeletal muscle is an important metric in the evaluation and diagnosis of musculoskeletal health. Measuring changes in muscle force exertion is essential for tracking the progress of athletes during training, for evaluating patients’ recovery after muscle injury, and also for assisting the diagnosis of conditions such as muscular dystrophy, multiple sclerosis, or Parkinson’s disease. Traditional hardware for strength evaluation requires technical training for operation, generates discrete time points for muscle assessment, and is implemented in controlled settings. The ability to continuously monitor muscle force without restricting the range of motion or adapting the exercise protocol to suit specific hardware would allow for a richer dataset that can help unlock critical features of muscle health and strength evaluation. In this paper, we employ wearable, ultra-sensitive soft strain sensors for tracking changes in muscle deformation during contractions. We demonstrate the sensors’ sensitivity to isometric contractions, as well as the sensors’ capacity to track changes in peak torque over the course of an isokinetic fatiguing protocol for the knee extensors. The wearable soft system was able to efficiently estimate peak joint torque reduction caused by muscle fatigue (mean NRMSE = 0.15±0.03).

Feature Selection and Reduction of Lower Limb Activity Recognition Based on Surface Electromyography and Motion Data

Daily activity recognition of lower limbs is of great significance to the health care of the elderly and patients with hemiplegia. Surface electromyography (sEMG) signal can directly reflect neuromuscular activity and is an important method for non-invasive monitoring of muscle activity on the body surface. In this paper, a novel method based on sEMG signal and inertial measurement unit (IMU) data to recognize daily activities of lower limbs is proposed. Record sEMG signals and IMU data of fifteen subjects using wearable sensor devices. After preprocessing such as filtering and sliding windows on the data, we extracted seventeen features. A feature selection method based on maximal relevance and minimal redundancy maximal relevance (mRMR) to select representative features. The selected features are input into four machine learning classifiers to classify four daily activities. The performance of the classifier is evaluated using accuracy and receiver operating characteristic curve-area under curve (ROC-AUC) score. The results show that the support vector machine has excellent performance in recognizing the daily activities of human lower limbs.