A Review of Techniques for Surface Electromyography Signal Quality Analysis

Farago, Emma; MacIsaac, Dawn; Suk, Michelle; Chan, Adrian D. C.

doi:10.1109/rbme.2022.3164797

Cited by 27 publications

(14 citation statements)

References 102 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The published literature [19,20,36,87] contains summary tables of all of the main factors that may affect correct signal estimation and the basic guidelines to follow for proper EMG recording. thetic, can be dangerous and misleading.…”

Section: Issues In Ergonomics Occupational Medicine and Sport Science...mentioning

confidence: 99%

Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors

Campanini

Merlo

Disselhorst‐Klug

et al. 2022

Sensors

View full text Add to dashboard Cite

Surface electromyography (sEMG) has been the subject of thousands of scientific articles, but many barriers limit its clinical applications. Previous work has indicated that the lack of time, competence, training, and teaching is the main barrier to the clinical application of sEMG. This work follows up and presents a number of analogies, metaphors, and simulations using physical and mathematical models that provide tools for teaching sEMG detection by means of electrode pairs (1D signals) and electrode grids (2D and 3D signals). The basic mechanisms of sEMG generation are summarized and the features of the sensing system (electrode location, size, interelectrode distance, crosstalk, etc.) are illustrated (mostly by animations) with examples that teachers can use. The most common, as well as some potential, applications are illustrated in the areas of signal presentation, gait analysis, the optimal injection of botulinum toxin, neurorehabilitation, ergonomics, obstetrics, occupational medicine, and sport sciences. The work is primarily focused on correct sEMG detection and on crosstalk. Issues related to the clinical transfer of innovations are also discussed, as well as the need for training new clinical and/or technical operators in the field of sEMG.

show abstract

Section: Issues In Ergonomics Occupational Medicine and Sport Science...mentioning

confidence: 99%

Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors

Campanini

Merlo

Disselhorst‐Klug

et al. 2022

Sensors

View full text Add to dashboard Cite

show abstract

“…As shown in Figure c, the dry AgNW on-skin electrode impedance is slightly higher than that of the Ag/AgCl gel electrode. However, when the conductive gel is used, the impedance of the AgNW on-skin electrode is significantly lower than that of the Ag/AgCl gel electrode, and it is more evident in the main frequency band of the sEMG signals (5–1000 Hz) . The reason for the low sheet resistance of the electrode resulting in the low contact impedance can be explained by the equivalent model of the skin-electrode interface shown in Figure S2, and the following equation can estimate the skin-electrode impedance: where R s is the sum of the resistance of the on-skin electrode and the electrical pathway between the electrodes in the skin surface tissue, the low sheet resistance of the AgNW on-skin electrode can effectively reduce R s and thus the skin-electrode impedance, and improve the quality of the sEMG signals …”

Section: Resultsmentioning

confidence: 99%

“…However, when the conductive gel is used, the impedance of the AgNW on-skin electrode is significantly lower than that of the Ag/AgCl gel electrode, and it is more evident in the main frequency band of the sEMG signals (5− 1000 Hz). 47 The reason for the low sheet resistance of the electrode resulting in the low contact impedance can be explained by the equivalent model of the skin-electrode interface shown in Figure S2, and the following equation can estimate the skin-electrode impedance: 46 (1)…”

Section: Evaluation Of Electrical Properties and Durability Of The Ag...mentioning

confidence: 99%

High-Fidelity sEMG Signals Recorded by an on-Skin Electrode Based on AgNWs for Hand Gesture Classification Using Machine Learning

Zou

Xue

et al. 2023

ACS Appl. Mater. Interfaces

View full text Add to dashboard Cite

The human forearm is one of the most densely distributed parts of the human body, with the most irregular spatial distribution of muscles. A number of specific forearm muscles control hand motions. Acquiring high-fidelity sEMG signals from human forearm muscles is vital for human-machine interface (HMI) applications based on gesture recognition. Currently, the most commonly used commercial electrodes for detecting sEMG or other electrophysiological signals have a rigid nature without stretchability and cannot maintain conformal contact with the human skin during deformation, and the adhesive hydrogel used in them to reduce skin-electrode impedance may shrink and cause skin inflammation after long-term use. Therefore, developing elastic electrodes with stretchability and biocompatibility for sEMG signal recording is essential for developing HMI. Here, we fabricated a nanocomposite hybrid on-skin electrode by infiltrating silver nanowires (AgNWs), a one-dimensional (1D) nano metal material with conductivity, into polydimethylsiloxane (PDMS), a silicone elastomer with a similar Young's modulus to that of the human skin. The AgNW on-skin electrode has a thickness of 300 μm and low sheet resistance of 0.481 ± 0.014 Ω/sq and can withstand the mechanical strain of up to 54% and maintain a sheet resistance lower than 1 Ω/sq after 1000 dynamic strain cycles. The AgNW on-skin electrode can record high signal-to-noise ratio (SNR) sEMG signals from forearm muscles and can reflect various force levels of muscles by sEMG signals. Besides, four typical hand gestures were recognized by the multichannel AgNW on-skin electrodes with a recognition accuracy of 92.3% using machine learning method. The AgNW on-skin electrode proposed in this study has great potential and promise in various HMI applications that employ sEMG signals as control signals.

show abstract

“…Surface EMG signals can be affected by different sources of noise (relative motion of soft tissues, bad mechanical or electrical connections, cross-talking between different muscles, etc…). Several works in literature provide solutions to this problem [ 43 , 44 ]. For our application, we took inspiration from [ 45 ] and we implemented the following filtering steps: (1) a first order low-pass Butterworth filter with a cutoff frequency of 500 Hz to reduce the high-frequency noise; (2) a first order high-pass Butterworth filter with a cutoff of 20 Hz, which allows us to remove the constant and slowly-changing behaviors; (3) the rectification of the filtered signal; and (4) another first order low-pass Butterworth filter, with a cutoff frequency of 1 Hz, for the extraction of the signal envelope.…”

Section: Methodsmentioning

confidence: 99%

A Multi-Modal Under-Sensorized Wearable System for Optimal Kinematic and Muscular Tracking of Human Upper Limb Motion

Paolo

Baracca

Menolotto

et al. 2023

Sensors

View full text Add to dashboard Cite

Wearable sensing solutions have emerged as a promising paradigm for monitoring human musculoskeletal state in an unobtrusive way. To increase the deployability of these systems, considerations related to cost reduction and enhanced form factor and wearability tend to discourage the number of sensors in use. In our previous work, we provided a theoretical solution to the problem of jointly reconstructing the entire muscular-kinematic state of the upper limb, when only a limited amount of optimally retrieved sensory data are available. However, the effective implementation of these methods in a physical, under-sensorized wearable has never been attempted before. In this work, we propose to bridge this gap by presenting an under-sensorized system based on inertial measurement units (IMUs) and surface electromyography (sEMG) electrodes for the reconstruction of the upper limb musculoskeletal state, focusing on the minimization of the sensors’ number. We found that, relying on two IMUs only and eight sEMG sensors, we can conjointly reconstruct all 17 degrees of freedom (five joints, twelve muscles) of the upper limb musculoskeletal state, yielding a median normalized RMS error of 8.5% on the non-measured joints and 2.5% on the non-measured muscles.

show abstract

A Review of Techniques for Surface Electromyography Signal Quality Analysis

Cited by 27 publications

References 102 publications

Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors

Fundamental Concepts of Bipolar and High-Density Surface EMG Understanding and Teaching for Clinical, Occupational, and Sport Applications: Origin, Detection, and Main Errors

High-Fidelity sEMG Signals Recorded by an on-Skin Electrode Based on AgNWs for Hand Gesture Classification Using Machine Learning

A Multi-Modal Under-Sensorized Wearable System for Optimal Kinematic and Muscular Tracking of Human Upper Limb Motion

Contact Info

Product

Resources

About