Wearable EMG Research Articles

A wearable EMG sensor for continuous wrist neuromuscular activity for monitoring

Carpal Tunnel Syndrome (CTS) is an entrapment neuropathy that affects 3-6% of the adult population globally. About 90% of nerve-damaging diseases are labeled CTS when diagnosed, making it one of the most prominent nerve dysfunctions in the current population metrics. CTS cases have soared in a world where digital gadgets are progressively gaining traction. There is also a high onset of CTS in laborers specializing in fine motor skills. A patient with CTS typically entails symptoms of pain, numbness, and tingling in the wrist. However, accurate diagnosis and consistent checkups for CTS are becoming a bigger issue. In this research paper, we demonstrate the utilization of various diagnostic tests in supporting CTS diagnosis and the necessity for a monitoring system to track wrist neuromuscular activity. By analyzing traditional assessment techniques and identifying their limitations, we can enhance diagnosis, making it easier for patients to deal with the condition. This approach has culminated in the development of our prototype, BracEMG. Through a wearable EMG sensor, our prototype aims to track possible signs of CTS or other nerve dysfunctions at the wrist. The findings gained through an EMG test support our research by outlining the prevalence of CTS across different age groups and illustrating the levels of muscular activity in a graphical format. Our results demonstrate overall muscle activity at the wrist. Since no universally accepted standard for diagnosing CTS exists, we envision our work as the starting point for more extensive research. The limitations of our prototype can become the foundation for future developments in the industry revolving around CTS.

Wearable high-density EMG sleeve for complex hand gesture classification and continuous joint angle estimation

High-density electromyography (HD-EMG) can provide a natural interface to enhance human–computer interaction (HCI). This study aims to demonstrate the capability of a novel HD-EMG forearm sleeve equipped with up to 150 electrodes to capture high-resolution muscle activity, decode complex hand gestures, and estimate continuous hand position via joint angle predictions. Ten able-bodied participants performed 37 hand movements and grasps while EMG was recorded using the HD-EMG sleeve. Simultaneously, an 18-sensor motion capture glove calculated 23 joint angles from the hand and fingers across all movements for training regression models. For classifying across the 37 gestures, our decoding algorithm was able to differentiate between sequential movements with 97.3±0.3%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$97.3 \\pm 0.3\\%$$\\end{document} accuracy calculated on a 100 ms bin-by-bin basis. In a separate mixed dataset consisting of 19 movements randomly interspersed, decoding performance achieved an average bin-wise accuracy of 92.8±0.8%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$92.8 \\pm 0.8\\%$$\\end{document}. When evaluating decoders for use in real-time scenarios, we found that decoders can reliably decode both movements and movement transitions, achieving an average accuracy of 93.3±0.9%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$93.3 \\pm 0.9\\%$$\\end{document} on the sequential set and 88.5±0.9%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$88.5 \\pm 0.9\\%$$\\end{document} on the mixed set. Furthermore, we estimated continuous joint angles from the EMG sleeve data, achieving a R2\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$R^2$$\\end{document} of 0.884±0.003\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.884 \\pm 0.003$$\\end{document} in the sequential set and 0.750±0.008\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$0.750 \\pm 0.008$$\\end{document} in the mixed set. Median absolute error (MAE) was kept below 10° across all joints, with a grand average MAE of 1.8±0.04∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$1.8 \\pm 0.04^\\circ$$\\end{document} and 3.4±0.07∘\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$3.4 \\pm 0.07^\\circ$$\\end{document} for the sequential and mixed datasets, respectively. We also assessed two algorithm modifications to address specific challenges for EMG-driven HCI applications. To minimize decoder latency, we used a method that accounts for reaction time by dynamically shifting cue labels in time. To reduce training requirements, we show that pretraining models with historical data provided an increase in decoding performance compared with models that were not pretrained when reducing the in-session training data to only one attempt of each movement. The HD-EMG sleeve, combined with sophisticated machine learning algorithms, can be a powerful tool for hand gesture recognition and joint angle estimation. This technology holds significant promise for applications in HCI, such as prosthetics, assistive technology, rehabilitation, and human–robot collaboration.

Identifying alterations in hand movement coordination from chronic stroke survivors using a wearable high-density EMG sleeve

Objective.Non-invasive, high-density electromyography (HD-EMG) has emerged as a useful tool to collect a range of neurophysiological motor information. Recent studies have demonstrated changes in EMG features that occur after stroke, which correlate with functional ability, highlighting their potential use as biomarkers. However, previous studies have largely explored these EMG features in isolation with individual electrodes to assess gross movements, limiting their potential clinical utility. This study aims to predict hand function of stroke survivors by combining interpretable features extracted from a wearable HD-EMG forearm sleeve.Approach.Here, able-bodied (N= 7) and chronic stroke subjects (N= 7) performed 12 functional hand and wrist movements while HD-EMG was recorded using a wearable sleeve. A variety of HD-EMG features, or views, were decomposed to assess alterations in motor coordination.Main Results.Stroke subjects, on average, had higher co-contraction and reduced muscle coupling when attempting to open their hand and actuate their thumb. Additionally, muscle synergies decomposed in the stroke population were relatively preserved, with a large spatial overlap in composition of matched synergies. Alterations in synergy composition demonstrated reduced coupling between digit extensors and muscles that actuate the thumb, as well as an increase in flexor activity in the stroke group. Average synergy activations during movements revealed differences in coordination, highlighting overactivation of antagonist muscles and compensatory strategies. When combining co-contraction and muscle synergy features, the first principal component was strongly correlated with upper-extremity Fugl Meyer hand sub-score of stroke participants (R2= 0.86). Principal component embeddings of individual features revealed interpretable measures of motor coordination and muscle coupling alterations.Significance.These results demonstrate the feasibility of predicting motor function through features decomposed from a wearable HD-EMG sleeve, which could be leveraged to improve stroke research and clinical care.

Compact BLE Antenna With a Modified PIFA Configuration for Wearable EMG Monitor

An antenna mounted underneath the top cover of a wearable device, fed by an ultra-low-profile connector, is proposed for Bluetooth-Low-Energy (BLE) communication at 2.4 GHz. As the antenna is to be used for a wearable device, it is essential that it should be compact in size and tolerant against the nearby medium change. The modified planar inverted-F antenna (PIFA) that we propose consists of a main patch and a parasitic element to broaden the antenna bandwidth. The end of the parasitic element is shorted to miniaturize the antenna to 25 by 10.8 millimeters, fitting well inside a watch-type wearable device. Also, the complete ground layer of the antenna makes it radiate well in the outward direction, and minimally interact with the back-side medium. This results in the reflection coefficient being insensitive to the medium change on the backside. The proposed antenna has a peak gain of 3.62 dBi along with 20% efficiency and the impedance bandwidth of 80 MHz (2.4 ~2.48 GHz). To examine the communicative operation of the antenna in practice, the received signal strength indicator (RSSI) of the complete prototype device with the antenna is measured in various postures and orientations, demonstrating reliable connectivity within a typical indoor distance of 10 meters. Lastly, the antenna is embedded in a wearable device, demonstrating electromyography’s wireless monitoring.

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.

A New Approach to Quantifying Muscular Fatigue Using Wearable EMG Sensors during Surgery: An Ergonomic Case Study.

(1) Background: Surgeons are exposed to musculoskeletal loads that are comparable to those of industrial workers. These stresses are harmful for the joints and muscles and can lead to musculoskeletal disorders (MSD) and working incapacity for surgeons. In this paper, we propose a novel ergonomic and visualization approach to assess muscular fatigue during surgical procedures. (2) Methods: The activity of eight muscles from the shoulder girdle and the cervical/lumbar spines were evaluated using position and electromyographic wearable sensors while a surgeon performed an arthroscopic rotator-cuff surgery on a patient. The time and frequency-domain variables of the root-mean-square amplitude and mean power frequency, respectively, were calculated from an electromyographic signal. (3) Results: The entire surgical procedure lasted 73 min and was divided into 10 sub-phases associated with specific level of muscular activity and fatigue. Most of the muscles showed activity above 60%, while the middle trapezius muscles were almost constantly activated (>20%) throughout the surgical procedure. (4) Conclusion: Wearable sensors can be used during surgical procedure to assess fatigue. Periods of low-to-high activity and fatigue can be evaluated and visualized during surgery. Micro-breaks throughout surgical procedures are suggested to avoid fatigue and to prevent the risk of developing MSD.

A biomechanical approach to study lunge in fence athletes

Fencing requires neuromuscular coordination, strength, and power, and it has a high impact on the musculoskeletal system [1–5]. The lunge is the most important strike action. It is produced with explosive power and surprises the opponent to score points. The lunge starts from the armed arm, which must be moved as quickly as possible toward the opponent, combined with lower body movements to provide a boosting action [4]. The present study aims at assessing kinematics and electromyographic differences between tasks and categories. The participants were 28 male and female, élite and recreational fencers, practicing foil and épée. The age ranged from 8 to 35 years old. Anthropometric and biographical information were taken. Kinematics data were acquired using a wearable inertial system BTS G-SENSOR2, positioned on the L5 vertebra. Four wearable EMG probes BTS FREEEMG 1000 were used to detect EMG signal of Deltoideus Anterior (DLTA) and Rectus femoris (RF) both on the armed side; Erector Longissimus muscle (LONG) and Gastrocnemius medialis (GAM), on the opposite one. These devices were synchronized by the acquisition software BTS EMG-Analyzer. Muscles' MVC and two trials of lunge (AF), step forward lunge (AA) and step backward lunge (DA) were taken. Data were processed with MATLAB and Tracker. VWilcoxon’s effect size for continuous variables and prevalence differences for categorical ones. Age and anthropometric measures were statistically different between the two categories (P < 0.05). No statistically significant differences are observed between the sexes, excluding thigh circumference (P < 0.05). The normalized distances and x-axis ROM between tasks and the y-axis ROM in the second trial (AA), the mean normalized muscle activation of DLTA, RF, and GAM between categories were statistically different (P <0.05). No significant statistical differences in muscle activation patterns were observed between different categories. Each correlation coefficient was interpreted based on a previously described classification using similar variables: 0-0.4 (weak), 0.4-0.7 (moderate), and 0.7-1.0 (strong). Table 1 summarizes the results observed from the correlation, the direction of the arrow stands for the direction of correlation (positive or negative), while the number of arrows is the intensity of correlation (weak, moderate, or strong). It is important to emphasize the negative correlation between lunge time and activation intensity of the LONG muscle, as well as the positive correlation between lunge distance and activation intensity of the GAM muscle. Differences have been observed in some kinematic and electromyographic parameters among categories and tasks. No substantial difference in muscle patterns is observed between different categories. Furthermore, correlations have emerged between muscular activity and spatiotemporal variables of movement: a greater rapidity of movement is associated with greater activity of the LONG, because it is balance responsible. A longer lunge distance is related to a greater activity of the GAM (boosting muscle).

Stretchable and Self-Healable Graphene-Polymer Conductive Composite for Wearable EMG Sensor.

In bioelectronics, stretchable and self-healable electrodes can reliably measure electrophysiological signals from the human body because they have good modulus matching with tissue and high durability. In particular, the polymer–graphene composite has advantages when it is used as an electrode for bioelectronic sensor devices. However, it has previously been reported that external stimuli such as heat or light are required for the self-healing process of polymer/graphene composites. In this study, we optimized a conducting composite by mixing a self-healing polymer (SHP) and graphene. The composite materials can not only self-heal without external stimulation but also have rapid electrical recovery from repeated mechanical damage such as scratches. In addition, they had stable electrical endurance even when the cyclic test was performed over 200 cycles at 50% strain, so they can be useful for a bioelectronic sensor device with high durability. Finally, we measured the electromyogram signals caused by the movement of arm muscles using our composite, and the measured data were transmitted to a microcontroller to successfully control the movement of the robot’s hand.

Synchronization of wearable motion capture and EMG measurement systems.

Synchronization of motion capture systems with other modalities in out-of-the-lab settings is not trivial. Various synchronization methods exist, such as using servers or transistor-transistor-logic pulses. However, not all measurement set-ups allow for such synchronization methods. Therefore, we have developed and validated an acceleration based post-measurement method to synchronize an IMU based motion capture system and an EMG measurement device. On top of the thigh IMU an additional accelerometer was placed which was connected to the analog input of the EMG device. By applying cross-correlation continuously, the similarities in the measured acceleration by the two measurement systems can be used for synchronization. We performed a validation measurement on seven able-bodied subjects and tested various correlation window sizes in hour long measurements in an out of the lab setting. It can be concluded that the developed method works for different activities when a suitable window length is chosen for cross-correlation. If no other options are available for synchronization, this correlation based method using an additional accelerometer is a viable option.

Evaluation of surgical skill using machine learning with optimal wearable sensor locations.

Evaluation of surgical skills during minimally invasive surgeries is needed when recruiting new surgeons. Although surgeons’ differentiation by skill level is highly complex, performance in specific clinical tasks such as pegboard transfer and knot tying could be determined using wearable EMG and accelerometer sensors. A wireless wearable platform has made it feasible to collect movement and muscle activation signals for quick skill evaluation during surgical tasks. However, it is challenging since the placement of multiple wireless wearable sensors may interfere with their performance in the assessment. This study utilizes machine learning techniques to identify optimal muscles and features critical for accurate skill evaluation. This study enrolled a total of twenty-six surgeons of different skill levels: novice (n = 11), intermediaries (n = 12), and experts (n = 3). Twelve wireless wearable sensors consisting of surface EMGs and accelerometers were placed bilaterally on bicep brachii, tricep brachii, anterior deltoid, flexor carpi ulnaris (FCU), extensor carpi ulnaris (ECU), and thenar eminence (TE) muscles to assess muscle activations and movement variability profiles. We found features related to movement complexity such as approximate entropy, sample entropy, and multiscale entropy played a critical role in skill level identification. We found that skill level was classified with highest accuracy by i) ECU for Random Forest Classifier (RFC), ii) deltoid for Support Vector Machines (SVM) and iii) biceps for Naïve Bayes Classifier with classification accuracies 61%, 57% and 47%. We found RFC classifier performed best with highest classification accuracy when muscles are combined i) ECU and deltoid (58%), ii) ECU and biceps (53%), and iii) ECU, biceps and deltoid (52%). Our findings suggest that quick surgical skill evaluation is possible using wearables sensors, and features from ECU, deltoid, and biceps muscles contribute an important role in surgical skill evaluation.

A Robust Multi-Channel EMG System for Lower Back and Abdominal Muscles Training

Abstract EMG is an established method to acquire the action potentials of contracted muscles. Although commercial EMG systems are available and it is one of the most researched biosignals, it has never become widely used in rehabilitation or fitness training monitoring. The reasons are technical challenges of wearable EMG systems regarding electrode placement, motion artefacts and the complex connectivity of multi-channel EMG measurements. We address this problem for the lower back and abdominal musculature, through a novel dry electrodes belt, multi-channel high density EMG circuitry and problem-specific signal processing. The subject can easily strap the dry electrodes belt around himself which provides 16 EMG channels. Interferences from the ECG and motion artefacts are reduced by a stationary wavelet decomposition. Afterwards, an inter-channel filter is applied to increase the robustness of the signals. Subject measurements during different kinds of typical abdominal and lower back training exercises were performed wearing the novel dry electrodes belt. The results show the possibility of robust EMG measurements from the lower back and abdominal muscles by utilizing the gathered redundancy, appropriately. The additional information obtained via the multi-channel EMG circuitry and spatial oversampling can be used to address current problems of EMG applications. It combines the advantages of robustness and the capability of using comfortable dry electrodes. Therefore, the proposed measurement method for acquiring spatial information about the muscle contractions from the lower trunk can be used for rehabilitation or fitness training monitoring.

A Novel Adaptive Mutation PSO Optimized SVM Algorithm for sEMG-Based Gesture Recognition

In the field of noncontact human-computer interaction, it is of crucial importance to distinguish different surface electromyography (sEMG) gestures accurately for intelligent prosthetic control. Gesture recognition based on low sampling frequency sEMG signal can extend the application of wearable low-cost EMG sensor (for example, MYO bracelet) in motion control. In this paper, a combination of sEMG gesture recognition consisting of feature extraction, genetic algorithm (GA), and support vector machine (SVM) model is proposed. Particularly, a novel adaptive mutation particle swarm optimization (AMPSO) algorithm is proposed to optimize the parameters of SVM; moreover, a new calculation method of mutation probability is also defined. The AMPSO-SVM model based on combination processing is successfully applied to MYO bracelet dataset, and four gesture classifications are carried out. Furthermore, AMPSO-SVM is compared with PSO-SVM, GS-SVM, and BP. The sEMG gesture recognition rate of AMPSO-SVM is 0.975, PSO-SVM is 0.9463, GS-SVM is 0.9093, and BP is 0.9019. The experimental results show that AMPSO-SVM is effective for low-frequency sEMG signals of different gestures.

ANN-based automated scaffold builder activity recognition through wearable EMG and IMU sensors

Construction worker activity recognition is essential for worker performance and safety assessment. With the development of wearable sensing technologies, many researchers developed kinematic sensor-based worker activity recognition methods with considerable accuracy. However, the limitations of the previous studies remain at the challenge of using smartphones for practical implementation, fewer classified activities, and limited recognized motions and body parts. This study proposes an ANN-based automated construction worker activity recognition method that can recognize complex construction activities. The proposed methodology discusses data acquisition, data fusion, and artificial neural network (ANN) model development. A case study of scaffold builder activities was investigated to validate the proposed methodology's feasibility and evaluate its performance compared to other existing methods. The results show that the proposed model can recognize fifteen scaffold builder activities with an accuracy of 94% with 0.94 weighted precision, recall, and F1 Score.

Transfer Learning-Based Muscle Activity Decoding Scheme by Low-frequency sEMG for Wearable Low-cost Application

The surface electromyogram (sEMG) contains a wealth of motion information, which can reflect user's muscle motion intentions. The decoding based on sEMG has been widely used to provide a safe and effective human-computer interaction (HCI) method for neural prosthesis and exoskeleton robot control. The motor intention decoding based on low sampling frequency sEMG may promote the application of wearable low-cost EMG sensors in HCI. Therefore, a motor intention decoding scheme suitable for low frequency EMG signal is proposed in this paper, that is, transfer learning based on Alexnet. Moreover, the effects of different feature extraction methods and data augmentation with Gaussian white noise are fully analyzed. The proposed algorithm is evaluated with the NinaPro database 5. The highest accuracy can reach 70.4%±4.36% in 53 gestures identification of 10 subjects. Some classical machine learning algorithms such as support vector machine (SVM), linear discriminant analysis (LDA) and K Nearest Neighbor (KNN) are chosen to make comparison, where the SVM with Gaussian kernel function reaches to the maximum accuracy of 67.98%±4.56%. Two-way variance results show significant differences between each other. The experiment results show that the transfer learning is effective for decoding low-frequency sEMG for a large number of gestures.

Quantitative Evaluation of Muscle Activity on Upper Limbs in Daily Activities Using Wearable EMG Sensor System

Quantitative Evaluation of Muscle Activity on Upper Limbs in Daily Activities Using Wearable EMG Sensor System

A Fall Risk Assessment Mechanism for Elderly People Through Muscle Fatigue Analysis on Data From Body Area Sensor Network

With the rapid growth and commercialization of wearable sensor technology, the research community foresees great potential of Body Area Sensor Network (BASN) applications in the healthcare industry, including elderly healthcare. We consider the issue of fall in elderly, which has severe consequences including death. Most of the related proposals in the literature are reactive. In this paper, we propose a proactive Fall Risk Assessment mechanism for elderly by performing Muscle Fatigue Analysis on data collected using BASN consisting of wearable sensors. We first discuss the in-house developed android application that connects with the wearable EMG sensors placed on the human body to collect data of four activities including walking, sitting and getting up from a chair, and ankle stretch (Dorsiflexion and planter flexion) in a controlled environment. Then we present our Fall Risk Assessment algorithm that computes the Fall Risk Assessment score based on various levels of muscle fatigue. We tested our algorithm using four features to determine muscle fatigue in two lower limb muscles i.e. Tibilias (TM) and Gastrocnemius (GM) muscles. The implementation results of various scenarios on Matlab are presented as the proof-of-concept. Our evaluation results of two lower limb muscles behavior in selected activities justify our claim that muscle fatigue in these two muscles could lead to falling in an elderly person.

Ictal quantitative surface electromyography correlates with postictal EEG suppression

ObjectiveTo test the hypothesis that neurophysiologic biomarkers of muscle activation during convulsive seizures reveal seizure severity and to determine whether automatically computed surface EMG parameters during seizures can predict postictal generalized EEG suppression (PGES), indicating increased risk for sudden unexpected death in epilepsy. Wearable EMG devices have been clinically validated for automated detection of generalized tonic-clonic seizures. Our goal was to use quantitative EMG measurements for seizure characterization and risk assessment.MethodsQuantitative parameters were computed from surface EMGs recorded during convulsive seizures from deltoid and brachial biceps muscles in patients admitted to long-term video-EEG monitoring. Parameters evaluated were the durations of the seizure phases (tonic, clonic), durations of the clonic bursts and silent periods, and the dynamics of their evolution (slope). We compared them with the duration of the PGES.ResultsWe found significant correlations between quantitative surface EMG parameters and the duration of PGES (p < 0.001). Stepwise multiple regression analysis identified as independent predictors in deltoid muscle the duration of the clonic phase and in biceps muscle the duration of the tonic-clonic phases, the average silent period, and the slopes of the silent period and clonic bursts. The surface EMG-based algorithm identified seizures at increased risk (PGES ≥20 seconds) with an accuracy of 85%.ConclusionsIctal quantitative surface EMG parameters correlate with PGES and may identify seizures at high risk.Classification of evidenceThis study provides Class II evidence that during convulsive seizures, surface EMG parameters are associated with prolonged postictal generalized EEG suppression.

Wearable EMG Bridge—A Multiple-Gesture Reconstruction System Using Electrical Stimulation Controlled by the Volitional Surface Electromyogram of a Healthy Forearm

In this study, a wearable prototype system was developed for multiple-gesture rehabilitation using electrical stimulation controlled by a volitional surface electromyography (sEMG) scan of a healthy forearm. The purpose of the prototype system is to reconstruct multiple gestures of a paralysed limb and to simplify the positioning of sEMG detection sites on a healthy forearm. A self-designed eight-channel sEMG detection armband was used to detect the sEMG signal distributions of the muscle groups in healthy forearms. Linear discriminant analysis (LDA) was used to classify the sEMG signal distributions corresponding to different gestures, and then the classification results were mapped to corresponding stimulation channels. The sEMG signal with the maximum root mean square (RMS) was used as the source of stimulus coding for each gesture. Our proposed mean absolute value (MAV)/number of slope sign changes (NSS) dual-coding (MNDC) algorithm was used to encode the sEMG signal into an electrical stimulus with a dynamic pulse width and frequency. The constant-current stimulation armband electrically stimulated multiple muscles in the affected forearm by means of a circuit designed with a time-division multiplexed stimulation channel. An experiment involving 6 able-bodied volunteers showed that when the detection armband was located near the middle of the forearm, the gesture classification accuracy was greater than 90%, and each active sEMG signal was high. Gesture bridge experiments, including grasping, wrist flexion, wrist extension and finger extension, were carried out among six hemiplegic subjects and between one able-bodied volunteer acting as a controller and each of six stroke patients as the controllee. Both sets of results show that the proposed system can reconstruct these four gestures in the controlled subject with a delay of at most 360 ms and with a correlation coefficient of >0.72.

Wearable EMG Shirt for Upper Limb Training.

This work presents a wearable shirt fitted with dry electrodes to measure EMG in upper limbs during physical exercise. The hardware acquires signals from 6 channels and transmits wirelessly to a PC via Bluetooth. In the PC, EMG signals are analyzed to give feedback to the user regarding the exercise being performed. To alert for muscle fatigue, the Dimitrov index is used, with much better results that classic mean or median EMG frequency tracking. The system is able to monitor EMG activity and provide valuable information in real-time for professional and amateur athletes and their sports coach, improving their training protocols.

Spotlight on the January 30 issue

#### Notable in Neurology ® this week This issue features an article that illustrates the importance of funduscopic examination in the emergency department for optic nerve head edema in patients presenting with headache, neurologic deficit, visual loss, or elevated blood pressure; and another that reports the accurate detection of generalized tonic-clonic seizures by a wearable surface EMG device. A featured Views & Reviews article accounts the barriers to overcoming the dementia challenge in Latin American countries. ### The Pediatric Stroke Outcome Measure: A predictor of outcome following arterial ischemic stroke In this prospective longitudinal observational cohort study, neurologic impairment in …