Journal articles: 'Surface Electromyography (sEMG)'

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Bolek, Jeffrey E. "Uncommon Surface Electromyography." Biofeedback 38, no. 2 (June 1, 2010): 52–55. http://dx.doi.org/10.5298/1081-5937-38.2.52.

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Abstract Surface electromyography (SEMG) can be used as a tool to help gain the return/discovery of motor function in those with disabilities. This article presents the case of “Joey,” an 18-month-old toddler. An already challenging case due to age is made even more difficult considering his genetically based multiple impairments. SEMG provided a window of opportunity, previously unavailable, to allow Joey to demonstrate the new motor skills that he was capable of learning.

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Arena, John G. "Future Directions in Surface Electromyography." Biofeedback 38, no. 2 (June 1, 2010): 78–82. http://dx.doi.org/10.5298/1081-5937-38.2.78.

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Abstract The use of surface electromyography (SEMG) has increased exponentially in the past four decades. SEMG is one of the most widespread measures employed today in psychophysiological assessment and one of three primary biofeedback modalities. This article briefly outlines three areas that the author believes are important for SEMG to address if it is to continue to flourish in the future: applications in telehealth, the use of telemetry and ambulatory monitoring, and studies on the stability or reliability of surface electromyography.

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HE, JINBAO, XINHUA YI, and ZAIFEI LUO. "CHARACTERIZATION OF MOTOR UNIT AT DIFFERENT STRENGTHS WITH MULTI-CHANNEL SURFACE ELECTROMYOGRAPHY." Journal of Mechanics in Medicine and Biology 17, no. 01 (February 2017): 1750024. http://dx.doi.org/10.1142/s0219519417500245.

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In this study, specific changes in electromyographic characteristics of individual motor units (MUs) associated with different muscle contraction forces are investigated using multi-channel surface electromyography (SEMG). The gradient convolution kernel compensation (GCKC) algorithm is employed to separate individual MUs from their surface interferential electromyography (EMG) signals and provide the discharge instants, which is later used in the spike-triggered averaging (STA) techniques to obtain the complete waveform. The method was tested on experimental SEMG signals acquired during constant force contractions of biceps brachii muscles in five subjects. Electromyographic characteristics including the recruitment number, waveform amplitude, discharge pattern and innervation zone (IZ) are studied. Results show that changes in the action potential of single MU with different contraction force levels are consistent with those for all MUs, and that the amplitude of MU action potentials (MUAPs) provides a useful estimate of the muscle contraction forces.

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Nacpil, Edric John Cruz, Rencheng Zheng, Tsutomu Kaizuka, and Kimihiko Nakano. "A surface electromyography controlled steering assistance interface." Journal of Intelligent and Connected Vehicles 2, no. 1 (August 29, 2019): 1–13. http://dx.doi.org/10.1108/jicv-11-2018-0011.

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Purpose Two-handed automobile steering at low vehicle speeds may lead to reduced steering ability at large steering wheel angles and shoulder injury at high steering wheel rates (SWRs). As a first step toward solving these problems, this study aims, firstly, to design a surface electromyography (sEMG) controlled steering assistance interface that enables hands-free steering wheel rotation and, secondly, to validate the effect of this rotation on path-following accuracy. Design/methodology/approach A total of 24 drivers used biceps brachii sEMG signals to control the steering assistance interface at a maximized SWR in three driving simulator scenarios: U-turn, 90º turn and 45º turn. For comparison, the scenarios were repeated with a slower SWR and a game steering wheel in place of the steering assistance interface. The path-following accuracy of the steering assistance interface would be validated if it was at least comparable to that of the game steering wheel. Findings Overall, the steering assistance interface with a maximized SWR was comparable to a game steering wheel. For the U-turn, 90º turn and 45º turn, the sEMG-based human–machine interface (HMI) had median lateral errors of 0.55, 0.3 and 0.2 m, respectively, whereas the game steering wheel, respectively, had median lateral errors of 0.7, 0.4 and 0.3 m. The higher accuracy of the sEMG-based HMI was statistically significant in the case of the U-turn. Originality/value Although production automobiles do not use sEMG-based HMIs, and few studies have proposed sEMG controlled steering, the results of the current study warrant further development of a sEMG-based HMI for an actual automobile.

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Ankrum, Dennis R. "Questions to ask When Interpreting Surface Electromyography (SEMG) Research." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 44, no. 30 (July 2000): 5–530. http://dx.doi.org/10.1177/154193120004403036.

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Surface electromyography (SEMG) is widely used to evaluate muscle activity. In SEMG, researchers attach electrodes to the surface of the skin overlying a muscle and measure the amount of electricity it produces as muscle fibers contract. SEMG can determine which muscles are active, their degree of activity, and how active the muscle is compared to the subject's capacity. It can also be used to estimate muscle force. Properly employed, SEMG assists in evaluating the relative risk of a work task. As articles reporting SEMG results are often used by ergonomics practitioners as guidance in job design, the ability to interpret SEMG research is critical. Problems occur when researchers assume their readers have a greater familiarity with SEMG than actually exists, or when they make any of a number of SEMG-related research or interpretation errors. This paper suggests some questions that should be asked when evaluating a study that reports SEMG data.

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Nacpil, Edric John Cruz, and Kimihiko Nakano. "Surface Electromyography-Controlled Automobile Steering Assistance." Sensors 20, no. 3 (February 2, 2020): 809. http://dx.doi.org/10.3390/s20030809.

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Disabilities of the upper limb, such as hemiplegia or upper limb amputation, can limit automobile drivers to steering with one healthy arm. For the benefit of these drivers, recent studies have developed prototype interfaces that realized surface electromyography (sEMG)-controlled steering assistance with path-following accuracy that has been validated with driving simulations. In contrast, the current study expands the application of sEMG-controlled steering assistance by validating the Myo armband, a mass-produced sEMG-based interface, with respect to the path-following accuracy of a commercially available automobile. It was hypothesized that one-handed remote steering with the Myo armband would be comparable or superior to the conventional operation of the automobile steering wheel. Although results of low-speed field testing indicate that the Myo armband had lower path-following accuracy than the steering wheel during a 90° turn and wide U-turn at twice the minimum turning radius, the Myo armband had superior path-following accuracy for a narrow U-turn at the minimum turning radius and a 45° turn. Given its overall comparability to the steering wheel, the Myo armband could be feasibly applied in future automobile studies.

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Coppeta, Luca, Sandro Gentili, Stefano Mugnaini, Ottavia Balbi, Stefano Massimiani, Gianluca Armieri, Antonio Pietroiusti, and Andrea Magrini. "Neuromuscular Functional Assessment in Low Back Pain by Surface Electromyography (SEMG)." Open Public Health Journal 12, no. 1 (February 28, 2019): 61–67. http://dx.doi.org/10.2174/1874944501912010061.

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Background: Low back pain is a major occupational health issue and a leading cause of disability globally. Significant differences in Surface Electromyography (SEMG) have been reported between persons with Low Back Pain (LBP) and normal, healthy controls. Many studies reveal that when the trunk is in full flexion there is an electrical silence in back muscles referred to as “flexion-relaxation phenomenon.” It is often absent in individuals reporting LBP and particularly chronic LBP. There are several SEMG measures that describe this phenomenon. Objective: To evaluate muscle activity in acute and chronic LBP and the usefulness of quick and reliable procedures to demonstrate abnormal electromyographic activity of the spine erector muscles. Methods: We evaluated 40 subjects aged 25-65 years. For each participant, a clinical history regarding the presence of chronic or acute LBP was collected. Each subject was evaluated with SEMG measures of spine erector muscles during standing and prone position (for acute LBP), and flex-extension movement (for chronic LBP subjects). Superficial potential was recorded and compared between groups. Results: In all three procedures, differences were identified in the surface electromyographic activity between the healthy controls and the one affected by LBP. Conclusion: The study of normal and pathologic electromyographic patterns could be a valid means to support in an objective way the presence/absence of acute and chronic LBP.

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Fang, Yinfeng, Honghai Liu, Gongfa Li, and Xiangyang Zhu. "A Multichannel Surface EMG System for Hand Motion Recognition." International Journal of Humanoid Robotics 12, no. 02 (May 27, 2015): 1550011. http://dx.doi.org/10.1142/s0219843615500115.

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Surface electromyography (sEMG)-based hand motion recognition has a variety of promising applications. While a person performs different hand motions, commands can be extracted to control external devices, such as prosthetic hands, tablets and so forth. The acquisition of discriminative sEMG signals determines the accuracy of intended control commands extraction. This paper develops an 16-channel sEMG signal acquisition system with a novel electrode configuration that is specially designed to collect sEMG on the forearm. Besides, to establish the relationship between multichannel sEMG signals and hand motions, a 2D EMG map is designed. Inspired from the electromyographic (EMG) map, this paper proposes an EMG feature named differential root mean square (DRMS) that somewhat takes the relationship between neighboring EMG channels into account. In the task of four hand motion discrimination by K-means and fuzzy C-means, DRMS outperforms traditional root mean square (RMS) by 29.0% and 36.8%, respectively. The findings of this paper support and guide the use of sEMG techniques to investigate sEMG-based hand motion recognition.

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Sella, Gabriel E. "Surface EMG (SEMG): A Synopsis." Biofeedback 47, no. 2 (June 1, 2019): 36–43. http://dx.doi.org/10.5298/1081-5937-47.1.05.

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Surface electromyography is an electrophysiological modality assessing the electrical activity of skeletal musculature. The Sella protocol is a structured assessment protocol, including static muscle assessment and dynamic muscle assessment, utilizing standardized electrode placements, conditions, and movements during assessment. This protocol can serve as a basis for designing biofeedback-assisted rehabilitation of patients with chronic pain and other musculoskeletal problems. The protocol can also be applied in forensic evaluations and in optimal performance settings.

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Zeng, Xiong, Ying Dong, and Xiaohao Wang. "Flexible Electrode by Hydrographic Printing for Surface Electromyography Monitoring." Materials 13, no. 10 (May 19, 2020): 2339. http://dx.doi.org/10.3390/ma13102339.

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Surface electromyography (sEMG) monitoring has recently inspired new applications in the field of patient diagnose, rehabilitation therapy, man–machine–interface and prosthesis control. However, conventional wet electrodes for sEMG recording cannot fully satisfy the requirements of these applications because they are based on rigid metals and conductive gels that cause signal quality attenuation, motion artifact and skin allergy. In this study, a novel flexible dry electrode is presented for sEMG monitoring. The electrode is fabricated by screen-printing a silver–eutectic gallium–indium system over a transfer tattoo paper, which is then hydrographically printed on 3D surface or human skin. Peano curve in open-network pattern is adopted to enhance the mechanics of the electrode. Hydrographic printing enables the electrode to attach to skin intimately and conformably, meanwhile assures better mechanical and electrical properties and therefore improves the signal quality and long-term wearability of the electrode. By recording sEMG signal of biceps under three kinds of movement with comparison to conventional wet electrode, the feasibility of the presented flexible dry electrode for sEMG monitoring was proved.

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Piitulainen, Harri, Alberto Botter, Mathieu Bourguignon, Veikko Jousmäki, and Riitta Hari. "Spatial variability in cortex-muscle coherence investigated with magnetoencephalography and high-density surface electromyography." Journal of Neurophysiology 114, no. 5 (November 1, 2015): 2843–53. http://dx.doi.org/10.1152/jn.00574.2015.

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Cortex-muscle coherence (CMC) reflects coupling between magnetoencephalography (MEG) and surface electromyography (sEMG), being strongest during isometric contraction but absent, for unknown reasons, in some individuals. We used a novel nonmagnetic high-density sEMG (HD-sEMG) electrode grid (36 mm × 12 mm; 60 electrodes separated by 3 mm) to study effects of sEMG recording site, electrode derivation, and rectification on the strength of CMC. Monopolar sEMG from right thenar and 306-channel whole-scalp MEG were recorded from 14 subjects during 4-min isometric thumb abduction. CMC was computed for 60 monopolar, 55 bipolar, and 32 Laplacian HD-sEMG derivations, and two derivations were computed to mimic “macroscopic” monopolar and bipolar sEMG (electrode diameter 9 mm; interelectrode distance 21 mm). With unrectified sEMG, 12 subjects showed statistically significant CMC in 91–95% of the HD-sEMG channels, with maximum coherence at ∼25 Hz. CMC was about a fifth stronger for monopolar than bipolar and Laplacian derivations. Monopolar derivations resulted in most uniform CMC distributions across the thenar and in tightest cortical source clusters in the left rolandic hand area. CMC was 19–27% stronger for HD-sEMG than for “macroscopic” monopolar or bipolar derivations. EMG rectification reduced the CMC peak by a quarter, resulted in a more uniformly distributed CMC across the thenar, and provided more tightly clustered cortical sources than unrectifed sEMGs. Moreover, it revealed CMC at ∼12 Hz. We conclude that HD-sEMG, especially with monopolar derivation, can facilitate detection of CMC and that individual muscle anatomy cannot explain the high interindividual CMC variability.

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Pietropaoli, Davide, Eleonora Ortu, Mario Giannoni, Ruggero Cattaneo, Alessandra Mummolo, and Annalisa Monaco. "Alterations in Surface Electromyography Are Associated with Subjective Masticatory Muscle Pain." Pain Research and Management 2019 (November 22, 2019): 1–9. http://dx.doi.org/10.1155/2019/6256179.

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Background. Tenderness of masseters and temporalis can be considered a relevant tool for diagnosis of myo-type craniofacial pain disorders, but a limit of pain score systems is that they are based on subjective pain perception. Surface electromyography (sEMG) is a noninvasive and reliable tool for recording muscle activity. Therefore, we investigated whether a correlation exists between tenderness on masseters and temporalis, assessed by subjective pain scale, and muscles activity, evaluated by sEMG, in patients with painful temporomandibular disorder (TMD) and concurrent tension-type headache (TTH). Methods. A cross-sectional study on fifty adult volunteer patients with TMD and TTH, who underwent tenderness protocol according to Diagnostic Criteria for TMD (DC/TMD) guidelines, was conducted followed by sEMG recording of temporalis and masseters. Pearson’s correlation was performed to investigate the correlation between muscular activity and subjective pain scores. Results. An overall moderate correlation between muscle tenderness and sEMG values (y = 1 + 1.2 · x; r2 = 0.62; p<0.0001), particularly in the temporalis, was observed. Segregation of data occurred according to tenderness and sEMG values. At the highest pain score, the mean sEMG absolute value was higher at the temporalis than the masseters. Conclusions. Our study provides evidence that subjective pain perception can be objectively quantified at a magnitude proportional to pain severity. At greater tenderness scores, higher sEMG activity at the level of temporalis could help discriminate clinically prevalent TTH versus prevalent TMD. sEMG confirms to be an accurate tool to reliably objectify the subjective perception of pain. When combined with clinical evaluation and patients’ symptoms, sEMG increases diagnostic sensitivity in the field of myo-type craniofacial pain disorders. This trial is registered with NCT02789085.

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Kaczmarek, Piotr, Tomasz Mańkowski, and Jakub Tomczyński. "putEMG—A Surface Electromyography Hand Gesture Recognition Dataset." Sensors 19, no. 16 (August 14, 2019): 3548. http://dx.doi.org/10.3390/s19163548.

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In this paper, we present a putEMG dataset intended for the evaluation of hand gesture recognition methods based on sEMG signal. The dataset was acquired for 44 able-bodied subjects and include 8 gestures (3 full hand gestures, 4 pinches and idle). It consists of uninterrupted recordings of 24 sEMG channels from the subject’s forearm, RGB video stream and depth camera images used for hand motion tracking. Moreover, exemplary processing scripts are also published. The putEMG dataset is available under a Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). The dataset was validated regarding sEMG amplitudes and gesture recognition performance. The classification was performed using state-of-the-art classifiers and feature sets. An accuracy of 90% was achieved for SVM classifier utilising RMS feature and for LDA classifier using Hudgin’s and Du’s feature sets. Analysis of performance for particular gestures showed that LDA/Du combination has significantly higher accuracy for full hand gestures, while SVM/RMS performs better for pinch gestures. The presented dataset can be used as a benchmark for various classification methods, the evaluation of electrode localisation concepts, or the development of classification methods invariant to user-specific features or electrode displacement.

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Saraiva, Bruno, Ester Silva, Rodrigo Polaquini Simões, Ana Paula Urdiales Garcia, Fabrício Augusto Menegon, Daniel Iwai Sakabe, Rodrigo Lício Ortolan, Luiz Eduardo Barreto Martins, Lucien Oliveira, and Aparecida Maria Catai. "Heart rate variability and surface electromyography of trained cyclists at different cadences." Motricidade 12, no. 1 (June 23, 2016): 43. http://dx.doi.org/10.6063/motricidade.4221.

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<p class="ResumoAbstract">The heart rate variability (HRV) and surface electromyography (sEMG) are important tools in the evaluation of cardiac autonomic system and neuromuscular parameters, respectively. The aim of the study was to evaluate the behavior of HRV and sEMG of the vastus lateralis in two exercise protocols on a cycle ergometer at 60 and 80 rpm. Eight healthy men cyclists who have trained for at least two years were evaluated. Reduction was observed followed by stabilization of RMSSD and SDNN indices of HRV (p<0.05) along with increases in the amplitude of the sEMG signal (p<0.05) in both protocols. Significant correlations were observed between the responses of HRV and sEMG in the cadence of 60 rpm (RMSSD and sEMG: r = -0.42, p=0.03; SDNN and sEMG: r = -0.45, p=0.01) and 80 rpm (RMSSD and sEMG: r = -0.47, p=0.02; SDNN and sEMG: r = -0.49, p=0.01), yet no difference was observed for these variables between the two protocols. We concluded that the parasympathetic cardiac responses and sEMG are independent of cadences applied at the same power output.</p>

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Szyszka-Sommerfeld, Liliana, Mariusz Lipski, and Krzysztof Woźniak. "Surface Electromyography as a Method for Diagnosing Muscle Function in Patients with Congenital Maxillofacial Abnormalities." Journal of Healthcare Engineering 2020 (September 22, 2020): 1–6. http://dx.doi.org/10.1155/2020/8846920.

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Electromyography (EMG) is the most objective and reliable method available for imaging muscle function and efficiency, which is done by identifying their electrical potentials. In global surface electromyography (sEMG), surface electrodes are located on the surface of the skin, and it detects superimposed motor unit action potentials from many muscle fibers. sEMG is widely used in orthodontics and maxillofacial orthopaedics to diagnose and treat temporomandibular disorders (TMD) in patients, assess stomatognathic system dysfunctions in patients with malocclusions, and monitor orthodontic therapies. Information regarding muscle sEMG activity in subjects with congenital maxillofacial abnormalities is limited. For this reason, the aim of this review is to discuss the usefulness of surface electromyography as a method for diagnosing muscle function in patients with congenital malformations of the maxillofacial region. Original papers on this subject, published in English between 1995 until 2020, are located in the MEDLINE/PubMed database.

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Cao, Tianao, Dan Liu, Qisong Wang, Ou Bai, and Jinwei Sun. "Surface Electromyography-Based Action Recognition and Manipulator Control." Applied Sciences 10, no. 17 (August 22, 2020): 5823. http://dx.doi.org/10.3390/app10175823.

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To improve the quality of lives of disabled people, the application of intelligent prosthesis was presented and investigated. In particular, surface Electromyography (sEMG) signals succeeded in controlling the manipulator in human–machine interface, due to the fact that EMG activity belongs to one of the most widely utilized biosignals and can reflect the straightforward motion intention of humans. However, the accuracy of real-time action recognition is usually low and there is usually obvious delay in a controlling manipulator, as a result of which the task of tracking human movement precisely, cannot be guaranteed. Therefore, this study proposes a method of action recognition and manipulator control. We built a multifunctional sEMG detection and action recognition system that integrated all discrete components. A biopotential measurement analog-to-digital converter with a high signal–noise rate (SNR) was chosen to ensure the high quality of the acquired sEMG signals. The acquired data were divided into sliding windows for processing in a shorter time. Mean Absolute Value (MAV), Waveform Length (WL), and Root Mean Square (RMS) were finally extracted and we found that compared to the Genetic-Algorithm-based Support Vector Machine (GA–SVM), the back propagation (BP) neural network performed better in joint action classification. The results showed that the average accuracy of judging the 5 actions (fist clenching, hand opening, wrist flexion, wrist extension, and calling me) was up to 93.2% and the response time was within 200 ms, which achieved a simultaneous control of the manipulator. Our work took into account the action recognition accuracy and real-time performance, and realized the sEMG-based manipulator control eventually, which made it easier for people with arm disabilities to communicate better with the outside world.

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Nacpil, Edric, Zheng Wang, Rencheng Zheng, Tsutomu Kaizuka, and Kimihiko Nakano. "Design and Evaluation of a Surface Electromyography-Controlled Steering Assistance Interface." Sensors 19, no. 6 (March 15, 2019): 1308. http://dx.doi.org/10.3390/s19061308.

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Millions of drivers could experience shoulder muscle overload when rapidly rotating steering wheels and reduced steering ability at increased steering wheel angles. In order to address these issues for drivers with disability, surface electromyography (sEMG) sensors measuring biceps brachii muscle activity were incorporated into a steering assistance system for remote steering wheel rotation. The path-following accuracy of the sEMG interface with respect to a game steering wheel was evaluated through driving simulator trials. Human participants executed U-turns with differing radii of curvature. For a radius of curvature equal to the minimum vehicle turning radius of 3.6 m, the sEMG interface had significantly greater accuracy than the game steering wheel, with intertrial median lateral errors of 0.5 m and 1.2 m, respectively. For a U-turn with a radius of 7.2 m, the sEMG interface and game steering wheel were comparable in accuracy, with respective intertrial median lateral errors of 1.6 m and 1.4 m. The findings of this study could be utilized to realize accurate sEMG-controlled automobile steering for persons with disability.

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Xie, Jing Jin, and Lei Zuo. "Design of SEMG Recognition System." Advanced Materials Research 403-408 (November 2011): 4194–98. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.4194.

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Surface electromyography (sEMG) is recorded from the surface of skeleton muscle by electrodes, it is the bioelectricity discharged by neuromuscular activities. This paper designed a data acquisition platform of sEMG, which contains hardware module and software module. The hardware contains electrodes, microcomputer, power and filters. The software contains data reading and storage, signal filter, timer and graphics user interface. It shows the system is workable with stable performance.

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Caffrey, Thomas R., and Robert Clasby. "Surface Electromyography-Assisted Ergonomic Analysis in a Newspaper Printing Plant: A Case Study." Biofeedback 38, no. 4 (January 1, 2010): 155–57. http://dx.doi.org/10.5298/1081-5937-38.4.06.

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Abstract This case study reports on the use of surface electromyography (SEMG) evaluation in a work environment, including production, to show a relationship between muscle dysfunction and specific job tasks and their injury potential. The results show that SEMG can help identify discordant muscle activity as part of an ergonomic evaluation. Such an evaluation leads to improvement in muscle function through SEMG-guided worker/workplace retraining.

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Chen, Lin, Jianting Fu, Yuheng Wu, Haochen Li, and Bin Zheng. "Hand Gesture Recognition Using Compact CNN via Surface Electromyography Signals." Sensors 20, no. 3 (January 26, 2020): 672. http://dx.doi.org/10.3390/s20030672.

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By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore, we designed a compact Convolution Neural Network (CNN) model, which not only improves the classification accuracy but also reduces the number of parameters in the model. Our proposed model was validated on the Ninapro DB5 Dataset and the Myo Dataset. The classification accuracy of gesture recognition achieved good results.

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Wang, You, Hengyang Wang, Huiyan Li, Asif Ullah, Ming Zhang, Han Gao, Ruifen Hu, and Guang Li. "Qualitative Recognition of Primary Taste Sensation Based on Surface Electromyography." Sensors 21, no. 15 (July 23, 2021): 4994. http://dx.doi.org/10.3390/s21154994.

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Based on surface electromyography (sEMG), a novel recognition method to distinguish six types of human primary taste sensations was developed, and the recognition accuracy was 74.46%. The sEMG signals were acquired under the stimuli of no taste substance, distilled vinegar, white granulated sugar, instant coffee powder, refined salt, and Ajinomoto. Then, signals were preprocessed with the following steps: sample augments, removal of trend items, high-pass filter, and adaptive power frequency notch. Signals were classified with random forest and the classifier gave a five-fold cross-validation accuracy of 74.46%, which manifested the feasibility of the recognition task. To further improve the model performance, we explored the impact of feature dimension, electrode distribution, and subject diversity. Accordingly, we provided an optimized feature combination that reduced the number of feature types from 21 to 4, a preferable selection of electrode positions that reduced the number of channels from 6 to 4, and an analysis of the relation between subject diversity and model performance. This study provides guidance for further research on taste sensation recognition with sEMG.

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Wang, Lin, Hong Wang, Rong Rong Fu, and Ning Ning Zhang. "Characteristic Parameters of Surface Electromyography Signals of Cervical Muscles." Applied Mechanics and Materials 249-250 (December 2012): 1308–12. http://dx.doi.org/10.4028/www.scientific.net/amm.249-250.1308.

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Surface electromyography (SEMG) signals of cervical muscles are investigated by time-frequency analysis and biomechanics analysis. Medium frequency (MF) and integrated electromyography (IEMG) are extracted and analyzed from SEMG signals of subjects’ upper trapezius. The Experimental results show that the value of MF decreases and the value of IEMG increases with the increase of fatigue of the vertical muscles. Also, the values of IEMG at different testing points of same cervical muscle are compared. The value of IEMG with higher resistant moment is higher than that with lower resistant moment. That means the muscle with high resistance moment is easier to be fatigue. This investigation is important for people, especially those who work/read with bowing head or before computer for a long time, to prevent cervical spondylosis.

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Vojtech, Jennifer M., Michael D. Chan, Bhawna Shiwani, Serge H. Roy, James T. Heaton, Geoffrey S. Meltzner, Paola Contessa, Gianluca De Luca, Rupal Patel, and Joshua C. Kline. "Surface Electromyography–Based Recognition, Synthesis, and Perception of Prosodic Subvocal Speech." Journal of Speech, Language, and Hearing Research 64, no. 6S (June 18, 2021): 2134–53. http://dx.doi.org/10.1044/2021_jslhr-20-00257.

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Purpose This study aimed to evaluate a novel communication system designed to translate surface electromyographic (sEMG) signals from articulatory muscles into speech using a personalized, digital voice. The system was evaluated for word recognition, prosodic classification, and listener perception of synthesized speech. Method sEMG signals were recorded from the face and neck as speakers with ( n = 4) and without ( n = 4) laryngectomy subvocally recited (silently mouthed) a speech corpus comprising 750 phrases (150 phrases with variable phrase-level stress). Corpus tokens were then translated into speech via personalized voice synthesis ( n = 8 synthetic voices) and compared against phrases produced by each speaker when using their typical mode of communication ( n = 4 natural voices, n = 4 electrolaryngeal [EL] voices). Naïve listeners ( n = 12) evaluated synthetic, natural, and EL speech for acceptability and intelligibility in a visual sort-and-rate task, as well as phrasal stress discriminability via a classification mechanism. Results Recorded sEMG signals were processed to translate sEMG muscle activity into lexical content and categorize variations in phrase-level stress, achieving a mean accuracy of 96.3% ( SD = 3.10%) and 91.2% ( SD = 4.46%), respectively. Synthetic speech was significantly higher in acceptability and intelligibility than EL speech, also leading to greater phrasal stress classification accuracy, whereas natural speech was rated as the most acceptable and intelligible, with the greatest phrasal stress classification accuracy. Conclusion This proof-of-concept study establishes the feasibility of using subvocal sEMG-based alternative communication not only for lexical recognition but also for prosodic communication in healthy individuals, as well as those living with vocal impairments and residual articulatory function. Supplemental Material https://doi.org/10.23641/asha.14558481

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Tsinganos, Panagiotis, Bruno Cornelis, Jan Cornelis, Bart Jansen, and Athanassios Skodras. "Data Augmentation of Surface Electromyography for Hand Gesture Recognition." Sensors 20, no. 17 (August 29, 2020): 4892. http://dx.doi.org/10.3390/s20174892.

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The range of applications of electromyography-based gesture recognition has increased over the last years. A common problem regularly encountered in literature is the inadequate data availability. Data augmentation, which aims at generating new synthetic data from the existing ones, is the most common approach to deal with this data shortage in other research domains. In the case of surface electromyography (sEMG) signals, there is limited research in augmentation methods and quite regularly the results differ between available studies. In this work, we provide a detailed evaluation of existing (i.e., additive noise, overlapping windows) and novel (i.e., magnitude warping, wavelet decomposition, synthetic sEMG models) strategies of data augmentation for electromyography signals. A set of metrics (i.e., classification accuracy, silhouette score, and Davies–Bouldin index) and visualizations help with the assessment and provides insights about their performance. Methods like signal magnitude warping and wavelet decomposition yield considerable increase (up to 16%) in classification accuracy across two benchmark datasets. Particularly, a significant improvement of 1% in the classification accuracy of the state-of-the-art model in hand gesture recognition is achieved.

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Xu, Zhuo Jun, Yan Tao Tian, Zhi Ming Yang, and Yang Li. "Pattern Recognition of Finger Joint Angle for Intelligent Bionic Hand Using sEMG." Applied Mechanics and Materials 448-453 (October 2013): 3561–65. http://dx.doi.org/10.4028/www.scientific.net/amm.448-453.3561.

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Finger joint angle pattern recognition is significant for the development of an intelligent bionic hand. It makes the intelligent prosthesis understand the users intension more accurately and complete movements better. Surface electromyography signals have been widely used in intelligent bionics prosthesis research and rehabilitation medicine due to its advantages like high efficiency, convenient collection and non-invasive access. An improved grid-search method using a support vector machine has been proposed for the finger joint angle pattern recognition issue in surface electromyography signals. Pattern recognition for surface electromyography signals of index finger movement and metacarpophalangeal joint angle has been performed. Better classification performance was achieved through screening of feature vector combined with an improved grid-search support vector machine classification algorithm.

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Adeel, Muhammad, Hung-Chou Chen, Bor-Shing Lin, Chien-Hung Lai, Chun-Wei Wu, Jiunn-Horng Kang, Jian-Chiun Liou, and Chih-Wei Peng. "Oxygen Consumption (VO2) and Surface Electromyography (sEMG) during Moderate-Strength Training Exercises." International Journal of Environmental Research and Public Health 19, no. 4 (February 16, 2022): 2233. http://dx.doi.org/10.3390/ijerph19042233.

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Oxygen consumption (VO2) during strength training can be predicted through surface electromyography (sEMG) of local muscles. This research aimed to determine relations between VO2 and sEMG of upper and lower body muscles to predict VO2 from sEMG during moderate-intensity strength training exercises. Of the 12 participants recruited, 11 were divided into two groups: untrained (n = 5; with no training experience) and trained (n = 6; with 2 months of training experience). On different days, each individual completed six training sessions. Each participant performed training sessions consisting of three types of dumbbell exercises: shoulder press, deadlift, and squat, while wearing a mask for indirect calorimetric measurements of VO2 using the Cortex Metalyzer 3B. sEMG measurements of the bilateral middle deltoid, lumbar erector spinae, quadriceps (rectus femoris), and hamstring (biceps femoris) muscles were recorded. The VO2 was predicted from sEMG root mean square (RMS) values of the investigated muscles during the exercise period using generalized estimating equation (GEE) modeling. The predicted models for the three types of exercises for the untrained vs. trained groups were shoulder press [QIC = 102, * p = 0.000 vs. QIC = 82, * p = 0.000], deadlift [QIC = 172, * p = 0.000 vs. QIC = 320, * p = 0.026], and squat [QIC = 76, * p = 0.000 vs. QIC = 348, * p = 0.001], respectively. It was observed that untrained vs. trained groups predicted GEE models [quasi-likelihood under an independence model criterion (QIC) = 368, p = 0.330 vs. QIC = 837, p = 0.058], respectively. The study obtained significant VO2 prediction models during shoulder press, deadlift, and squat exercises using the right and left middle deltoid, right and left lumbar erector spinae, left rectus femoris, and right and left biceps femoris sEMG RMS for the untrained and trained groups during moderate-intensity strength training exercises.

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Pham, Tri, and Nicholas Kelling. "Mechanical and Membrane Keyboard Typing Assessment Using Surface Electromyography (sEMG)." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 59, no. 1 (September 2015): 912–15. http://dx.doi.org/10.1177/1541931215591268.

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Blanc, Yves, and Ugo Dimanico. "Electrode Placement in Surface Electromyography (sEMG) ”Minimal Crosstalk Area“ (MCA)." Open Rehabilitation Journal 3, no. 1 (January 1, 2010): 110–26. http://dx.doi.org/10.2174/1874943701003010110.

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Szyszka-Sommerfeld, Liliana, Magdalena Sycińska-Dziarnowska, Agata Budzyńska, and Krzysztof Woźniak. "Accuracy of Surface Electromyography in the Diagnosis of Pain-Related Temporomandibular Disorders in Children with Awake Bruxism." Journal of Clinical Medicine 11, no. 5 (February 28, 2022): 1323. http://dx.doi.org/10.3390/jcm11051323.

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The study assessed masticatory muscle electromyographic (EMG) activity in both children diagnosed with pain-related temporomandibular disorders (TMD-P) and awake bruxism (AB) and in children without TMD, as well as the diagnostic value of surface electromyography (sEMG) in diagnosing TMD-P in subjects with AB. After evaluation based on the Axis I of the Research Diagnostic Criteria for Temporomandibular Disorders (RDC/TMD), 30 children diagnosed with myofascial pain were included in the myofascial pain group and 30 children without TMD diagnosis comprised the control group (mean age of 9.49 ± 1.34 years). The activity of the anterior temporal (TA) and masseter (MM) muscle was assessed bilaterally using a DAB-Bluetooth device (zebris Medical GmBH, Germany) at rest and during maximum voluntary clenching (MVC). The receiver operating characteristic (ROC) curve was used to determine the accuracy, sensitivity, and specificity of the normalized sEMG data. Statistically significant intergroup differences were observed in TA and MM muscle EMG activity at rest and during MVC. Moderate degree of sEMG accuracy in discriminating between TMD-P and non-TMD children was observed for TAmean, left MM, and MMmean EMG muscle activity at rest. sEMG can be a useful tool in assessing myofascial TMD pain in patients with AB.

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Wang, Junhong, Lipeng Wang, Xugang Xi, Seyed M. Miran, and Anke Xue. "Estimation and Correlation Analysis of Lower Limb Joint Angles Based on Surface Electromyography." Electronics 9, no. 4 (March 26, 2020): 556. http://dx.doi.org/10.3390/electronics9040556.

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Many people lose their motor function because of spinal cord injury or stroke. This work studies the patient’s continuous movement intention of joint angles based on surface electromyography (sEMG), which will be used for rehabilitation. In this study, we introduced a new sEMG feature extraction method based on wavelet packet decomposition, built a prediction model based on the extreme learning machine (ELM) and analyzed the correlation between sEMG signals and joint angles based on the detrended cross-correlation analysis. Twelve individuals participated in rehabilitation tasks, to test the performance of the proposed method. Five channels of sEMG signals were recorded, and denoised by the empirical mode decomposition. The prediction accuracy of the wavelet packet feature-based ELM prediction model was found to be 96.23% ± 2.36%. The experimental results clearly indicate that the wavelet packet feature and ELM is a better combination to build a prediction model.

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Bolek, Jeffrey E. "QSEMG: Quantitative Surface Electromyography: Origins and Applications in Physical Rehabilitation." Biofeedback 48, no. 2 (June 1, 2020): 26–31. http://dx.doi.org/10.5298/1081-5937-48.2.01.

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In a review of the topic, Pollock, Baer, Pomeroy, and Langhorne (2009) concluded that surface electromyography (SEMG) biofeedback does not appear to have any positive benefit for recovery for loss of motor function. One would expect that a modality that can provide a window onto muscle functioning would be widely used as a means to restore motor function after injury. However, in 2019 little had changed regarding the use of SEMG. It is not commonly found in the armamentarium of treatment options in the typical physical/occupational therapy clinic. The reasons for this are multifaceted, but can be summarized as lack of knowledge or application of the conditions required for success. This article will attempt to encourage the reader to engage in further study for one very important reason—it enables successful treatment.

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Feng, Yongfei, Mingwei Zhong, Xusheng Wang, Hao Lu, Hongbo Wang, Pengcheng Liu, and Luige Vladareanu. "Active triggering control of pneumatic rehabilitation gloves based on surface electromyography sensors." PeerJ Computer Science 7 (April 19, 2021): e448. http://dx.doi.org/10.7717/peerj-cs.448.

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The portable and inexpensive hand rehabilitation robot has become a practical rehabilitation device for patients with hand dysfunction. A pneumatic rehabilitation glove with an active trigger control system is proposed, which is based on surface electromyography (sEMG) signals. It can trigger the hand movement based on the patient’s hand movement trend, which may improve the enthusiasm and efficiency of patient training. Firstly, analysis of sEMG sensor installation position on human’s arm and signal acquisition process were carried out. Then, according to the statistical law, three optimal eigenvalues of sEMG signals were selected as the follow-up neural network classification input. Using the back propagation (BP) neural network, the classifier of hand movement is established. Moreover, the mapping relationship between hand sEMG signals and hand actions is built by training and testing. Different patients choose the same optimal eigenvalues, and the calculation formula of eigenvalues’ amplitude is unique. Due to the differences among individuals, the weights and thresholds of each node in the BP neural network model corresponding to different patients are not the same. Therefore, the BP neural network model library is established, and the corresponding network is called for operation when different patients are trained. Finally, based on sEMG signal trigger, the pneumatic glove training control algorithm was proposed. The combination of the trigger signal waveform and the motion signal waveform indicates that the pneumatic rehabilitation glove is triggered to drive the patient’s hand movement. Preliminary tests have confirmed that the accuracy rate of trend recognition for hand movement is about 90%. In the future, clinical trials of patients will be conducted to prove the effectiveness of this system.

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Dorgham, Osama, Ibrahim Al-Mherat, Jawdat Al-Shaer, Sulieman Bani-Ahmad, and Stephen Laycock. "Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network." Future Internet 11, no. 1 (January 21, 2019): 25. http://dx.doi.org/10.3390/fi11010025.

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Bioelectric signals are used to measure electrical potential, but there are different types of signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles. The current work aims to model and reproduce surface EMG (SEMG) signals using an artificial neural network. Such research can aid studies into life enhancement for those suffering from damage or disease affecting their nervous system. The SEMG signal is collected from the surface above the bicep muscle through dynamic (concentric and eccentric) contraction with various loads. In this paper, we use time domain features to analyze the relationship between the amplitude of SEMG signals and the load. We extract some features (e.g., mean absolute value, root mean square, variance and standard deviation) from the collected SEMG signals to estimate the bicep’ muscle force for the various loads. Further, we use the R-squared value to depict the correlation between the SEMG amplitude and the muscle loads by linear fitting. The best performance the ANN model with 60 hidden neurons for three loads used (3 kg, 5 kg and 7 kg) has given a mean square error of 1.145, 1.3659 and 1.4238, respectively. The R-squared observed are 0.9993, 0.99999 and 0.99999 for predicting (reproduction step) of smooth SEMG signals.

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Hou, Wensheng, Xiaoying Wu, Jun Zheng, Li Ma, Xiaolin Zheng, Yingtao Jiang, Dandan Yang, Shizhi Qian, and Chenglin Peng. "CHARACTERIZATION OF FINGER ISOMETRIC FORCE PRODUCTION WITH MAXIMUM POWER OF SURFACE ELECTROMYOGRAPHY." Biomedical Engineering: Applications, Basis and Communications 21, no. 03 (June 2009): 193–99. http://dx.doi.org/10.4015/s1016237209001258.

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Finger's action has been controlled by both intrinsic and extrinsic hand muscles. Characterizing the finger action with the activations of hand muscles could be useful for evaluating the neuromuscular control strategy of finger's motor functions. This study is designed to explore the correlation of isometric fingertip force production and frequency-domain features of surface electromyography (sEMG) recorded on extrinsic hand muscles. To this end, 13 subjects (five male and eight female university students) have been recruited to conduct a target force-tracking task. Each subject is required to produce a certain level of force with either the index or middle fingertip to match the pseudo-random ordered target force level (4N, 6N, or 8N) as accurate as possible. During the finger force production process, the sEMG signals are recorded on two extrinsic hand muscles: flex digitorum superficials (FDS) and extensor digitorum (ED). For each sEMG trail, the power spectrum is estimated with the autoregressive (AR) model and from which the maximum power is obtained. Our experimental results reveal three findings: (1) the maximum power increases with the force level regardless of the force producing finger (i.e. index or middle) and the extrinsic hand muscle (i.e. FDS or ED). (2) The sEMG maximum power of index finger is significantly lower than that of the middle finger under the same force level and extrinsic hand muscle. (3) No significant difference can be found between the maximum powers of FDS and ED. The results indicate that the activations of the extrinsic muscles are affected by both the force level and the force producing finger. Based on our findings, the sEMG maximum power of the extrinsic hand muscles could be used as a key parameter to describe the finger's actions.

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WIDJAJA, FERDINAN, CHENG YAP SHEE, WEI TECH ANG, WING LOK AU, and PHILIPPE POIGNET. "SENSING OF PATHOLOGICAL TREMOR USING SURFACE ELECTROMYOGRAPHY AND ACCELEROMETER FOR REAL-TIME ATTENUATION." Journal of Mechanics in Medicine and Biology 11, no. 05 (December 2011): 1347–71. http://dx.doi.org/10.1142/s0219519411004344.

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Tremor is the most common movement disorder and it is affecting more and more people as the world is aging. The cost involved is big considering the financial and social impact. This paper explores an assistive technology solution for upper limb pathological tremor compensation. Using both surface electromyography (SEMG) and accelerometer (ACC), a real-time pathological tremor compensation with functional electrical stimulation (FES) is proposed. One advantage of using SEMG is the electromechanical delay (SEMG data precedes the ACC data by 20–100 ms). Hence by detecting the tremor in advance, there is enough time window to do the necessary computation and to actuate the antagonist muscle by FES. This is also possible because the time taken for FES to actuate the muscle is significantly less than that of the neural signal, as detected by SEMG. For estimation of tremor parameters and separation between voluntary motion and tremor, an algorithm based on extended Kalman filter (EKF) is proposed. Experimental result from one essential tremor patient has shown 57% reduction in tremor power as measured by the ACC.

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Wang, Hengyang, Dongcheng Lu, Li Liu, Han Gao, Rumeng Wu, Yueling Zhou, Qing Ai, You Wang, and Guang Li. "Quantitatively Recognizing Stimuli Intensity of Primary Taste Based on Surface Electromyography." Sensors 21, no. 21 (October 20, 2021): 6965. http://dx.doi.org/10.3390/s21216965.

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A novel approach to quantitatively recognize the intensity of primary taste stimuli was explored based on surface electromyography (sEMG). We captured sEMG samples under stimuli of primary taste with different intensities and quantitatively recognized preprocessed samples with Support Vector Machine (SVM). The feasibility of quantitatively recognizing the intensity of Sour, Bitter, and Salty was verified. The sEMG signals were acquired under the stimuli of citric acid (aq), sucrose (aq), magnesium chloride (aq), sodium chloride (aq), and sodium glutamate (aq) with different concentrations, for five types of primary tastes: Sour, Sweet, Bitter, Salty, and Umami, whose order was fixed in this article. The acquired signals were processed with a method called Quadratic Variation Reduction to remove baseline wandering, and an adaptive notch to remove power frequency interference. After extracting 330 features for each sample, an SVM regressor with five-fold cross-validation was performed and the model reached R2 scores of 0.7277, 0.1963, 0.7450, 0.7642, and 0.5055 for five types of primary tastes, respectively, which manifested the feasibilities of the quantitative recognitions of Sour, Bitter, and Salty. To explore the facial responses to taste stimuli, we summarized and compared the muscle activities under stimuli of different taste types and taste intensities. To further simplify the model, we explored the impact of feature dimensionalities and optimized the feature combination for each taste in a channel-wise manner, and the feature dimensionality was reduced from 330 to 210, 120, 210, 260, 170 for five types of primary tastes, respectively. Lastly, we analyzed the model performance on multiple subjects and the relation between the model’s performance and the number of experiment subjects. This study can provide references for further research and applications on taste stimuli recognition with sEMG.

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Suprijanto, Suprijanto, Azizah S. Noor Azizah S. Noor, Miranti I. Mandasari Miranti I. Mandasari, and Hesty Susanti Hesty Susanti. "Surface Electromyography Quantification Methods for Evaluating Muscle Activity in Dysphagia." Sains Malaysiana 50, no. 12 (December 31, 2021): 3523–35. http://dx.doi.org/10.17576/jsm-2021-5012-05.

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Quantitative evaluation of stroke patients with the risk of swallowing disorder or dysphagia is required to support diagnosis and further rehabilitation planning. Fluoroscopy X-ray imaging usually is used for swallowing diagnosis, though it gives radiation exposure to patients. Therefore, quantification of muscle coordination patterns involved in swallowing based on surface electromyography (sEMG) was introduced. However, an adequate quantification of sEMG for dysphagia diagnosis still lacks standardization. In this work, potential sEMG signal features, namely the contraction duration (DUR), the time to peak of maximum contraction (TTP), and the total RMS power (TP), were further investigated to evaluate the swallowing processes in healthy subjects and post-stroke patients. The experimental scheme instructed the participant, i.e. 20 healthy subjects and 20 patients, to swallow 3 mL of water in normal swallowing mode and swallow saliva in dry swallowing mode. The proposed signal processing procedure helps to establish the feature extraction of the three features mentioned earlier. For dysphagia assessment, with the support of our proposed signal processing procedure, DUR and TTP can be used together to improve diagnosis reliability. The characteristic of both features in healthy subjects was shorter than in post-stroke patients. Also, the TP feature is useful as additional information to evaluate the role of suprahyoid (SUP) and infrahyoid (INF) muscle groups which are very important in the swallowing process. These results are promising to provide a reliable set of features in the time domain for swallowing analysis. Notably, this can also be utilized as a feature for supporting the automatic classification of dysphagia diagnosis.

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Miró, Francisco, Alfonso M. Galisteo, Juan L. Garrido-Castro, and Joaquín Vivo. "Surface Electromyography of the Longissimus and Gluteus Medius Muscles in Greyhounds Walking and Trotting on Ground Flat, Up, and Downhill." Animals 10, no. 6 (June 3, 2020): 968. http://dx.doi.org/10.3390/ani10060968.

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In the field of canine rehabilitation, knowledge of muscle function in the therapeutic exercises prescribed is needed by physical therapists and veterinary surgeons. To gain insight into the function of longissimus dorsi (LD) and gluteus medius (GM) muscles in dogs, five Greyhounds performing leash walking and trotting on the ground flat, up (+7%), and downhill (−7%) were studied by surface electromyography, and the mean and maximum activity was compared. For the same incline, the surface electromyography (sEMG) of LD was higher (p < 0.05) at the trot than at the walk. In LD muscle, trotting uphill showed significantly higher maximum activity than any other exercise. A change of +7% incline or −7% decline affected (increased or decreased, respectively) the mean sEMG of the LD and GM muscles of dogs walking or trotting on the ground. When combined, the influence of gait and incline on electromyographic activity was analyzed, and walking at certain inclines showed no difference with trotting at certain inclines. Walking and trotting up and downhill added separate therapeutic value to flat motion. The results of the present study might contribute to a better understanding of the function of LD and GM muscles in dogs, this being especially useful for the field of canine rehabilitation.

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Reinvee, Märt, and Mati Pääsuke. "Overview of Contemporary Low-cost sEMG Hardware for Applications in Human Factors and Ergonomics." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 60, no. 1 (September 2016): 408–12. http://dx.doi.org/10.1177/1541931213601092.

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For decades, surface electromyography (sEMG) has been one of the essential methods of Human Factors and Ergonomics. Although capturing sEMG data is relatively easy, proper interpretation of acquired data is possible only with sufficient background in electrophysiology, muscle mechanics and muscle functions. As the last decade has made biosignal acquisition more accessible, the current overview discusses the intrinsic properties of contemporary low-cost sEMG acquisition systems and proposes applications of sEMG for Human Factors and Ergonomics.

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YANG, D. D., W. S. HOU, X. Y. WU, J. ZHENG, X. L. ZHENG, Y. T. JIANG, and L. MA. "IMPACT OF FINGERTIP ACTIONS ON TOTAL POWER OF SURFACE ELECTROMYOGRAPHY FROM EXTRINSIC HAND MUSCLES." Journal of Mechanics in Medicine and Biology 12, no. 03 (June 2012): 1250056. http://dx.doi.org/10.1142/s0219519411004800.

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Quantizing the relationship between finger force and multitendoned extrinsic hand muscles could be useful for understanding the control strategies that underlie the coordination of finger movements and forces. The objective of this study is to explore the relationship of fingertip force production and total power of surface electromyography (sEMG) recorded on extrinsic hand muscles under isometric voluntary contraction. Thirteen healthy volunteers were recruited to participate in this study. In the designed force-tracking tasks, all volunteers were required to produce a certain force with either index finger or middle finger to match the target force for 5 s. Meanwhile, the sEMG signals were acquired from two extrinsic hand muscles: extensor digitorum (ED) and flexor digitorum superficialis (FDS). For each trial, sEMG of the effective force segment was extracted; then, the power spectrum was estimated based on autoregressive (AR) model and from which the corresponding total power of sEMG was computed. The experimental results reveal that the total power of sEMG linearly increases with force level regardless of the task finger and extrinsic hand muscle. It is also found that the total power obtained from index finger is significantly less than that of middle finger for FDS at the same force level (p < 0.05), while this kind of statistical significance cannot be found for ED. However, with respect to the measurement of total power, the type of extrinsic hand muscle has not exhibited significantly different contribution to the task finger under a certain fingertip force level. The findings of this study indicate that the total power of the extrinsic hand muscle's sEMG can be used to characterize finger's activities.

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Gaudet, Guillaume, Maxime Raison, Fabien Dal Maso, Sofiane Achiche, and Mickael Begon. "Intra- and Intersession Reliability of Surface Electromyography on Muscles Actuating the Forearm During Maximum Voluntary Contractions." Journal of Applied Biomechanics 32, no. 6 (December 2016): 558–70. http://dx.doi.org/10.1123/jab.2015-0214.

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The aim of this study is to determine the intra- and intersession reliability of nonnormalized surface electromyography (sEMG) on the muscles actuating the forearm during maximum voluntary isometric contractions (MVIC). A subobjective of this study is to determine the intra- and intersession reliability of forearm MVIC force or torque, which is a prerequisite to assess sEMG reliability. Eighteen healthy adults participated at 4 different times: baseline, 1-h post, 6-h post, and 24-h post. They performed 3 MVIC trials of forearm flexion, extension, pronation, and supination. sEMG of the biceps brachii short head, brachialis, brachioradialis, triceps brachii long head, pronator teres, and pronator quadratus were measured. The intraclass correlation coefficient (ICC) on MVIC ranged from 0.36 to 0.99. Reliability was excellent for flexion, extension, and supination MVIC for both intra- and intersession. The ICC on sEMG ranged from 0.58 to 0.99. sEMG reliability was excellent for brachialis, brachioradialis, and pronator quadratus, and good to excellent for triceps brachii, biceps brachii, and pronator teres. This study shows that performing 3 MVICs is sufficient to obtain highly reliable maximal sEMG over 24 h for the main muscles actuating the forearm. These results confirm the potential of sEMG for muscle motor functional monitoring.

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Gao, Jianxin, Cheng Han, Wenying Huang, Jianjun Gao, and Liaoliang Nie. "Experimental study on the sEMG of "joint angle effect" of human muscle strength—Taking biceps brachii as an example." E3S Web of Conferences 145 (2020): 01021. http://dx.doi.org/10.1051/e3sconf/202014501021.

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In the experiment, the author used wave plus wireless surface electromyography system (SEMs + 3-axis acceleration sensor) made in Italy and wave wireless EMG software system, high-definition high-speed camera and human joint angle measuring instrument. Taking human biceps brachii as an example, the static and dynamic isometric contraction of biceps brachii was completed surface electromyography. In the experiment, the surface electromyography of biceps brachii was measured at 30°, 60°, 90°, 120°, 150°, 180° and the surface electromyography of biceps brachii was measured at the same time when the biceps brachii was not loaded or when the biceps brachii was loaded. Secondly, the surface electromyography of biceps brachii was measured at the same time when the biceps brachii completed the whole process of flexion and extension of the elbow (centripetal and centrifugal). Finally, the paper combined with HD The effect of joint angle on the contraction of biceps brachii muscle was analyzed by camera technique. The results show that the static contraction force of biceps brachii is different when the elbow joint is at different angles; in addition, when the dynamic contraction, the contraction force of biceps brachii is inversely proportional to the angle of elbow joint.

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Gamucci, Fiorenza, Marcello Pallante, Sybille Molle, Enrico Merlo, and Andrea Bertuglia. "A Preliminary Study on the Use of HD-sEMG for the Functional Imaging of Equine Superficial Muscle Activation during Dynamic Mobilization Exercises." Animals 12, no. 6 (March 20, 2022): 785. http://dx.doi.org/10.3390/ani12060785.

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Superficial skeletal muscle activation is associated with an electric activity. Bidimensional High-Density Surface Electromyography (HD-sEMG) is a non-invasive technique that uses a grid of equally spaced electrodes applied on the skin surface to detect and portray superficial skeletal muscle activation. The goal of the study was to evaluate the feasibility of HD-sEMG to detect electrical activation of skeletal muscle and its application during rehabilitation exercises in horses. To fulfil this aim, activation of the superficial descending pectoral and external abdominal oblique core muscles were measured using HD-sEMG technology during dynamic mobilization exercises to induce lateral bending and flexion/extension tasks of the trunk. Masseter muscle was instrumented during mastication as a control condition. A 64 surface EMG channel wireless system was used with a single 64 electrode grid or a pair of 32 electrode grids. HD-sEMG provided unique information on the muscular activation onset, duration, and offset, along each motor task, and permitting inferences about the motor control strategy actuated by the central nervous system. Signals were further processed to obtain firing frequencies of few motor-neurons. Estimation of electromyographic amplitude and spectral parameters allowed detecting the onset of muscular fatigue during the motor tasks performed. HD-sEMG allows the assessment of muscular activation in horses performing specific motor tasks, supporting its future application in clinical and research settings.

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Ding, Shuai, Liang Wang, Zhan Peng Sun, Wei Jin Gao, and Shou Long Fang. "Movement Identification Based on Transient sEMG for Control of Prosthesis." Advanced Materials Research 971-973 (June 2014): 1651–54. http://dx.doi.org/10.4028/www.scientific.net/amr.971-973.1651.

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Researches on surface electromyography (sEMG) for upper-limb prosthesis control have been going on for several years. Most published studies on prosthesis usually use the steady-state sEMG or the transient sEMG for identification. However, the transient sEMG is less stable than steady-state sEMG. The nonstationarity in transient sEMG greatly affects the performance of myoelectric control. In this paper, we propose a method based on sparse representation to capture the characteristics of transient sEMG to identify movements. Experiment results show the proposed method extracts the variations in transient sEMG activity from different movements effectively. The proposed feature achieves a satisfactory classification rate, which outperforms the other features.

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Yang, Chun, Jinyi Long, and Hao Wang. "Performance Comparison of Classification Methods for Surface EMG-Based Human-Machine Interface." International Journal of Grid and High Performance Computing 7, no. 4 (October 2015): 47–56. http://dx.doi.org/10.4018/ijghpc.2015100104.

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Reliable control of assistive devices through surface electromyography (sEMG) based human-machine interfaces (HMIs) requires accurate classification of multi-channel sEMG. The design of effective pattern classification methods is one of the main challenges for sEMG-based HMIs. In this paper, the authors compared comprehensively the performance of different linear and nonlinear classifiers for the pattern classification of sEMG with respect to three pairs of upper-limb motions (i.e., hand close vs. hand open, wrist flexion vs. wrist extension, and forearm pronation vs. forearm supination). A feature selection approach based on information gain was also performed to reduce the muscle channels. Overall, the results showed that the linear classifiers produce slightly better classification performance, with or without the muscle channel selection.

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Edmonds, Harvey L., Lawrence J. Couture, Markku P. J. Paloheimo, and Rigor Benjamin M. "Objective assessment of opioid action by facial muscle surface electromyography (SEMG)." Progress in Neuro-Psychopharmacology and Biological Psychiatry 12, no. 5 (January 1988): 727–38. http://dx.doi.org/10.1016/0278-5846(88)90018-8.

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Ozdemir, Mehmet Akif, Deniz Hande Kisa, Onan Guren, and Aydin Akan. "Dataset for multi-channel surface electromyography (sEMG) signals of hand gestures." Data in Brief 41 (April 2022): 107921. http://dx.doi.org/10.1016/j.dib.2022.107921.

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Veer, Karan. "Spectral and mathematical evaluation of electromyography signals for clinical use." International Journal of Biomathematics 09, no. 06 (August 2, 2016): 1650094. http://dx.doi.org/10.1142/s1793524516500947.

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The surface electromyography (SEMG) is a complicated biomedical signal, generated during voluntary or involuntary muscle activities and these muscle activities are always controlled by the nervous system. In this paper, the processing and analysis of SEMG signals at multiple muscle points for different operations were carried out. Myoelectric signals were detected using designed acquisition setup which consists of an instrumentation amplifier, filter circuit, an amplifier with gain adjustment. Further, Labview[Formula: see text]-based data programming code was used to record SEMG signals for independent activities. The whole system consists of bipolar noninvasive electrodes, signal acquisition protocols and signal conditioning at different levels. This work uses recorded SEMG signals generated by biceps and triceps muscles for four different arm activities. Feature extraction was done on the recorded signal for investigating the voluntary muscular contraction relationship for exercising statistic measured index method to evaluate distance between two independent groups by directly addressing the quality of signal in separability class for different arm movements. Thereafter repeated factorial analysis of variance technique was implemented to evaluate the effectiveness of processed signal. From these results, it demonstrates that the proposed method can be used as SEMG feature evaluation index.

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Bolek, Jeffrey E., and Jennifer Yost. "Motor Control Recovery After a Severe Brain Injury: Applications of Quantitative Surface Electromyography." Biofeedback 41, no. 2 (June 1, 2013): 50–55. http://dx.doi.org/10.5298/1081-5937-41.2.03.

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In this case study, surface electromyography (SEMG) was used to help a severely brain-damaged adolescent regain head control. In addition to relearning a lost motor skill, the patient, because of the extensiveness of the injury, had to overcome deficits in memory, visual processing, and cognitive tone. The process of quantitative SEMG was used to teach the patient to use a targeted series of muscles, which, in 14 weeks, brought her to the point that for many activities a headrest was no longer needed.

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Pravečková, P., P. Matošková, V. Süss, and R. Jebavý. "Analysis of the specific strengthening exercises for the softball pitches using surface electromyography." Studia Kinanthropologica 17, no. 3 (September 30, 2016): 385–93. http://dx.doi.org/10.32725/sk.2016.093.

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Journal articles on the topic 'Surface Electromyography (sEMG)'

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