Tutorial. Surface electromyogram (sEMG) amplitude estimation: Best practices

Clancy, Edward A.; Morin, Evelyn; Hajian, Gelareh; Merletti, Roberto

doi:10.1016/j.jelekin.2023.102807

Cited by 9 publications

(4 citation statements)

References 115 publications

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“…A surface electromyogram (sEMG) is used to record the electrical activity produced by muscle contraction, examine neuromuscular activity, and assist in the interpretation of kinematic and kinetic information [7]. In the intricacies between the neuromuscular system and motion, sEMG offers significant data pertaining to activation patterns, muscular strength, and the phenomenon of fatigue [8].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models

Liu,

Ting,

Wang

et al. 2024

Sensors

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Gait analysis has been studied over the last few decades as the best way to objectively assess the technical outcome of a procedure designed to improve gait. The treating physician can understand the type of gait problem, gain insight into the etiology, and find the best treatment with gait analysis. The gait parameters are the kinematics, including the temporal and spatial parameters, and lack the activity information of skeletal muscles. Thus, the gait analysis measures not only the three-dimensional temporal and spatial graphs of kinematics but also the surface electromyograms (sEMGs) of the lower limbs. Now, the shoe-worn GaitUp Physilog® wearable inertial sensors can easily measure the gait parameters when subjects are walking on the general ground. However, it cannot measure muscle activity. The aim of this study is to measure the gait parameters using the sEMGs of the lower limbs. A self-made wireless device was used to measure the sEMGs from the vastus lateralis and gastrocnemius muscles of the left and right feet. Twenty young female subjects with a skeletal muscle index (SMI) below 5.7 kg/m2 were recruited for this study and examined by the InBody 270 instrument. Four parameters of sEMG were used to estimate 23 gait parameters. They were measured using the GaitUp Physilog® wearable inertial sensors with three machine learning models, including random forest (RF), decision tree (DT), and XGBoost. The results show that 14 gait parameters could be well-estimated, and their correlation coefficients are above 0.800. This study signifies a step towards a more comprehensive analysis of gait with only sEMGs.

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Section: Introductionmentioning

confidence: 99%

Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models

Liu,

Ting,

Wang

et al. 2024

Sensors

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“…sEMG is not an out of reach technical challenges (12,13,15,16), but its implementation for teaching can be found on www.robertomerletti. it, the YouTube channel NeuromechTV, the BiomeCast (17), or ISEK-JEK tutorials (8,(18)(19)(20).…”

Section: Introductionmentioning

confidence: 99%

Local experience of laboratory activities in a BS physical therapy course: integrating sEMG and kinematics technology with active learning across six cohorts

De la Fuente,

Neira,

Machado

et al. 2024

Front. Neurol.

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IntroductionIntegrating technology and active learning methods into Laboratory activities would be a transformative educational experience to familiarize physical therapy (PT) students with STEM backgrounds and STEM-based new technologies. However, PT students struggle with technology and feel comfortable memorizing under expositive lectures. Thus, we described the difficulties, uncertainties, and advances observed by faculties on students and the perceptions about learning, satisfaction, and grades of students after implementing laboratory activities in a PT undergraduate course, which integrated surface-electromyography (sEMG) and kinematic technology combined with active learning methods.MethodsSix cohorts of PT students (n = 482) of a second-year PT course were included. The course had expositive lectures and seven laboratory activities. Students interpreted the evidence and addressed different motor control problems related to daily life movements. The difficulties, uncertainties, and advances observed by faculties on students, as well as the students’ perceptions about learning, satisfaction with the course activities, and grades of students, were described.ResultsThe number of students indicating that the methodology was “always” or “almost always,” promoting creative, analytical, or critical thinking was 70.5% [61.0–88.0%]. Satisfaction with the whole course was 97.0% [93.0–98.0%]. Laboratory grades were linearly associated to course grades with a regression coefficient of 0.53 and 0.43 R-squared (p < 0.001).ConclusionIntegrating sEMG and kinematics technology with active learning into laboratory activities enhances students’ engagement and understanding of human movement. This approach holds promises to improve teaching-learning processes, which were observed consistently across the cohorts of students.

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“…T HE surface electromyogram (EMG) is the electrical signal generated by a muscle during its contraction and recorded by electrodes placed over the skin [1]. These electrodes have a large pick-up volume [2], [3], so they record a mixture of contributions from the target muscle (i.e., the one to be investigated) and nearby muscles [4]- [6]. The latter is known as crosstalk [7].…”

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confidence: 99%

“…More specifically, some results showed that a quantification through cross-correlation of the signals of target and crosstalk muscles is not effective [23]. Furthermore, a temporal filter was found to be not useful to reduce this undesired contribution [6], [24]. The easiest strategy to avoid this issue is through an appropriate electrode positioning [25] in combination with spatial filters [18], [20].…”

mentioning

confidence: 99%

Reduction of Crosstalk in the Electromyogram: Experimental Validation of the Optimal Spatio-Temporal Filter

Raggi,

Boccia,

Mesin

2023

IEEE Access

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Objective: Crosstalk in surface electromyogram (EMG) is an important open problem and the common strategy of reducing it through spatial filters needs improvements. Methods: We evaluated experimentally the optimal spatio-temporal filter (OSTF), i.e., a recent approach that adapts to the subject, filtering different EMG channels both in time and space to emphasize the signal of a target muscle discarding that of adjacent ones. EMGs were recorded by a high-density recording system from pronator teres (target muscle) and flexor carpi radialis (crosstalk muscle) of 8 healthy subjects. OSTF was tested in different conditions, considering one channel per muscle (either single or double differential, SD and DD, respectively), changing the selectivity of detection (small electrodes close to each other, or large ones with higher inter-electrode distance), the force applied by the muscles (whose EMGs were summed to simulate different levels of crosstalk), and the duration of the signal to train the method. Results: OSTF was less affected by crosstalk than SD and DD filters. Statistically significant improvements were obtained in reducing the crosstalk-induced variations: for example, considering small electrodes, we obtained a percentage error of 157.30±57.11 % and 38.54±10.47 % (mean±std) in the estimation of the average rectified value (ARV), and an error of 23.57±3.92 % and 8.31±0.88 % in the estimation of the median frequency (MDF), for SD and OSTF, respectively. Conclusion: The OSTF can be applied in real-time, is easy to use, and is feasible even when using only few detection channels, as is customary in many applications.

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Tutorial. Surface electromyogram (sEMG) amplitude estimation: Best practices

Cited by 9 publications

References 115 publications

Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models

Estimation of Gait Parameters for Adults with Surface Electromyogram Based on Machine Learning Models

Local experience of laboratory activities in a BS physical therapy course: integrating sEMG and kinematics technology with active learning across six cohorts

Reduction of Crosstalk in the Electromyogram: Experimental Validation of the Optimal Spatio-Temporal Filter

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