Surface Electromyography (<scp>sEMG</scp>)

Merletti, Roberto; Farina, Dario; Holobar, Aleš

doi:10.1002/047134608x.w8371

Cited by 14 publications

(17 citation statements)

References 94 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…It is widely used in medical rehabilitation, human-machine interface, biomedical research and other fields. The current applications of EMG mostly regard physiological investigations, monitoring of neurological disorders, planning of treatments, assessment of interventions and control of prostheses and robots [73]. Electromyography can be performed using two electrodes: surface (or skin) electrodes or inserted (or wire and needle) electrodes.…”

Section: Emg Signal Featuresmentioning

confidence: 99%

Biopotential Signal Monitoring Systems in Rehabilitation: A Review

Palumbo

Vizza

Calabrese

et al. 2021

Sensors

View full text Add to dashboard Cite

Monitoring physical activity in medical and clinical rehabilitation, in sports environments or as a wellness indicator is helpful to measure, analyze and evaluate physiological parameters involving the correct subject’s movements. Thanks to integrated circuit (IC) technologies, wearable sensors and portable devices have expanded rapidly in monitoring physical activities in sports and tele-rehabilitation. Therefore, sensors and signal acquisition devices became essential in the tele-rehabilitation path to obtain accurate and reliable information by analyzing the acquired physiological signals. In this context, this paper provides a state-of-the-art review of the recent advances in electroencephalogram (EEG), electrocardiogram (ECG) and electromyogram (EMG) signal monitoring systems and sensors that are relevant to the field of tele-rehabilitation and health monitoring. Mostly, we focused our contribution in EMG signals to highlight its importance in rehabilitation context applications. This review focuses on analyzing the implementation of sensors and biomedical applications both in literature than in commerce. Moreover, a final review discussion about the analyzed solutions is also reported at the end of this paper to highlight the advantages of physiological monitoring systems in rehabilitation and individuate future advancements in this direction. The main contributions of this paper are (i) the presentation of interesting works in the biomedical area, mainly focusing on sensors and systems for physical rehabilitation and health monitoring between 2016 and up-to-date, and (ii) the indication of the main types of commercial sensors currently being used for biomedical applications.

show abstract

Section: Emg Signal Featuresmentioning

confidence: 99%

Biopotential Signal Monitoring Systems in Rehabilitation: A Review

Palumbo

Vizza

Calabrese

et al. 2021

Sensors

View full text Add to dashboard Cite

show abstract

“…The sEMG signals however are prone to electrical and physiological noise [1,2], which can be mitigated by filters in the spatial and temporal domains.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the time-consuming nature of training a neural network, it is imperative to consider the potential effects of electrode count, spatial and temporal filtering. The frequency bandwidth of the sEMG is between approximately 10 and 500 Hz [1,2,6]. However, state-of-the-art myocontrol algorithms [7][8][9][10][11] rectify the signal and then apply low-pass filters below 5-20 Hz or compute the root mean squared (RMS) values from time intervals of 100-300 ms, which is comparable to said filters.…”

Section: Introductionmentioning

confidence: 99%

Influence of spatio-temporal filtering on hand kinematics estimation from high-density EMG signals ^*

Sîmpetru,

Cnejevici,

Farina

et al. 2024

J. Neural Eng.

View full text Add to dashboard Cite

Objective: Surface electromyography (sEMG) is a non-invasive technique that records the electrical signals generated by muscles through electrodes placed on the skin. sEMG is the state-of-the-art method used to control active upper limb prostheses because of the association between its amplitude and the neural drive sent from the spinal cord to muscles. However, accurately estimating the kinematics of a freely moving human hand using sEMG from extrinsic hand muscles remains a challenge. Deep learning has been recently successfully applied to this problem by mapping raw sEMG signals into kinematics. Nonetheless, the optimal number of EMG signals and the type of pre-processing that would maximize performance have not been investigated yet. Approach: Here, we analyze the impact of these factors on the accuracy in kinematics estimates. For this purpose, we processed monopolar sEMG signals that were originally recorded from 320 electrodes over the forearm muscles of 13 subjects. We used a previously published deep learning method that can map the kinematics of the human hand with real-time resolution. Main results: While myocontrol algorithms essentially use the temporal envelope of the EMG signal as the only EMG feature, we show that our approach requires the full bandwidth of the signal in the temporal domain for accurate estimates. Spatial filtering however, had a smaller impact and low-order spatial filters may be suitable. Moreover, reducing the number of channels by ablation resulted in large performance losses.The highest accuracy was reached with the highest number of available sensors (n=320). Importantly and unexpected, our results suggest that increasing the number of channels above those used in this study may further enhance the accuracy in predicting the kinematics of the human hand. Significance: We conclude that full bandwidth high-density EMG systems of hundreds of electrodes are needed for accurate kinematic estimates of the human hand.

show abstract

“…There are other medical International Journal of Intelligent Engineering and Systems, Vol. 16 conditions like dysarthria that cause speech impairment and some speech recognition research has been done for that [9]. But in this paper we are focussing mainly on laryngectomy patients with a hope that the methods developed can further be modified in the future to suit other speech impairment cases.…”

Section: Introductionmentioning

confidence: 99%

Application of Fractal Analysis based Feature Extractor for Channel Reduction of Silent Speech Interface Using Facial Electromyography

2023

IJIES

View full text Add to dashboard Cite

Surface electromyography (sEMG) based silent speech interface (SSI) is an actively investigated topic among the broad area of human computer interaction studies which is currently dominated by acoustic sound based speech recognition research. This research is an attempt to help people who have an impaired vocal system if they are having no issues with their facial muscle functions. The basic idea is to reduce the total number of sEMG electrodes that has to be affixed on the face thereby reducing the invasiveness of the silent speech recognition module. This is achieved by incorporating a new detrended fluctuation analysis (DFA) based feature along with the already existing features associated with electromyographic signals. DFA is used for the first time in literature in the area of surface electromyography based silent speech recognition. The main idea is to incorporate the DFA feature along with the state-of-the-art features to improve the performance of a sEMG based SSI model so that an efficient channel reduced model can be realised. Different channel combinations were tried to analyse the impact of each channel in word recognition accuracy and the optimal channel combination was identified. As a result of this research work, a reduced channel setup with 5 electrodes was proposed in place of the conventional 7 channel data acquisition setup. This was achieved while maintaining an accuracy of 83.88 % and 92.92 % using the decision tree (DT) model and K-nearest neighbours (KNN) model respectively.

show abstract

Surface Electromyography (sEMG)

Cited by 14 publications

References 94 publications

Biopotential Signal Monitoring Systems in Rehabilitation: A Review

Biopotential Signal Monitoring Systems in Rehabilitation: A Review

Influence of spatio-temporal filtering on hand kinematics estimation from high-density EMG signals ^*

Application of Fractal Analysis based Feature Extractor for Channel Reduction of Silent Speech Interface Using Facial Electromyography

Contact Info

Product

Resources

About

Surface Electromyography (sEMG)

Cited by 14 publications

References 94 publications

Biopotential Signal Monitoring Systems in Rehabilitation: A Review

Biopotential Signal Monitoring Systems in Rehabilitation: A Review

Influence of spatio-temporal filtering on hand kinematics estimation from high-density EMG signals *

Application of Fractal Analysis based Feature Extractor for Channel Reduction of Silent Speech Interface Using Facial Electromyography

Contact Info

Product

Resources

About

Influence of spatio-temporal filtering on hand kinematics estimation from high-density EMG signals ^*