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Electromyography Signal Acquisition and Processing for Movement Analysis

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A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Biosensors".

Deadline for manuscript submissions: closed (30 June 2022) | Viewed by 70879

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Special Issue Editors

Dr. Francesco Di Nardo

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Department of Information Engineering, Università Politecnica delle Marche, 60121 Ancona, Italy
Interests: biomedical signal processing (filtering, feature extraction, pattern recognition, time–frequency analysis, machine learning applications); interpretation (clinics, rehabilitation, sport); acquisition and processing of surface electromyography (EMG) signals to assess muscular function during gait tasks
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Dr. Valentina Agostini

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Department of Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
Interests: human motion analysis; motion tracking; gait analysis; wearable sensors; surface electromyography (EMG); motor control; biomechanics; neurorehabilitation; muscle synergies; medical signal processing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Silvia Conforto

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Department of Industrial, Electronic and Mechanical Engineering, University Roma Tre, 00146 Rome, Italy
Interests: signal and image processing in the biomedical field; human motion analysis; motor control

Special Issue Information

Dear Colleagues,

The assessment of muscle recruitment is acknowledged as one of the main issues of movement analysis. Muscle activity is typically monitored by surface electromyography (sEMG), a non-invasive technique widely adopted both in research and clinical settings. Recent advancements in commercial EMG signal acquisition technologies and sensors, the development of high-density surface EMG systems, the introduction of sensor fusion, and the availability of data storage and file sharing systems have changed the perspective about measuring, capturing, and analysing EMG signals, specifically in movement analysis. The areas of application are also increasing and differentiating. Besides typical fields such as basic research, clinics, and sports, EMG analysis is increasingly proposed in novel scenarios related to robotics, exoskeleton technology, prosthetics, assistive devices, electrical stimulation, and ergonomics.

The present Special Issue is designed to comprehensively cover the open research issues related to the improvement of classic approaches and the development of innovative technology and methodology for EMG signal acquisition and processing in the domain of movement analysis. Furthermore, the Special Issue aims to also focus on different fields of application of EMG analysis, including clinics, physiology, rehabilitation, sports, and ergonomics. Computational intelligence methods, such as machine and deep learning, have recently emerged as promising tools for the development and application of intelligent systems in interpreting EMG signals, and contributions on this field are also welcome.

This Special Issue includes, but is not limited to, the following topics:

Surface EMG
EMG sensors
EMG modelling
EMG feature extraction
EMG pattern recognition
High-density surface EMG
Machine and deep learning for EMG-based classification
EMG in clinical gait analysis
EMG analysis and interpretation for motor rehabilitation
EMG in sports and exercise

Dr. Francesco Di Nardo
Dr. Valentina Agostini
Prof. Dr. Silvia Conforto
Guest Editors

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Keywords

surface EMG
movement analysis
EMG sensors
biomedical signal processing
muscle recruitment

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Published Papers (12 papers)

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Research

17 pages, 4004 KiB

Open AccessArticle

A Simulation Study to Assess the Factors of Influence on Mean and Median Frequency of sEMG Signals during Muscle Fatigue

by Giovanni Corvini and Silvia Conforto

Sensors 2022, 22(17), 6360; https://doi.org/10.3390/s22176360 - 24 Aug 2022

Cited by 2 | Viewed by 2580

Abstract

Mean and Median frequency are typically used for detecting and monitoring muscle fatigue. These parameters are extracted from power spectral density whose estimate can be obtained by several techniques, each one characterized by advantages and disadvantages. Previous works studied how the implementation settings can influence the performance of these techniques; nevertheless, the estimation results have never been fully evaluated when the power density spectrum is in a low-frequency zone, as happens to the surface electromyography (sEMG) spectrum during muscle fatigue. The latter is therefore the objective of this study that has compared the Welch and the autoregressive parametric approaches on synthetic sEMG signals simulating severe muscle fatigue. Moreover, the sensitivity of both the approaches to the observation duration and to the level of noise has been analyzed. Results showed that the mean frequency greatly depends on the noise level, and that for Signal to Noise Ratio (SNR) less than 10dB the errors make the estimate unacceptable. On the other hand, the error in calculating the median frequency is always in the range 2–10 Hz, so this parameter should be preferred in the tracking of muscle fatigue. Results show that the autoregressive model always outperforms the Welch technique, and that the 3rd order continuously produced accurate and precise estimates; consequently, the latter should be used when analyzing severe fatiguing contraction. Full article

(This article belongs to the Special Issue Electromyography Signal Acquisition and Processing for Movement Analysis)

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17 pages, 2968 KiB

Open AccessArticle

Muscle Co-Contraction Detection in the Time–Frequency Domain

by Francesco Di Nardo, Martina Morano, Annachiara Strazza and Sandro Fioretti

Sensors 2022, 22(13), 4886; https://doi.org/10.3390/s22134886 - 28 Jun 2022

Cited by 8 | Viewed by 4273

Abstract

Background: Muscle co-contraction plays a significant role in motion control. Available detection methods typically only provide information in the time domain. The current investigation proposed a novel approach for muscle co-contraction detection in the time–frequency domain, based on continuous wavelet transform (CWT). Methods: In the current study, the CWT-based cross-energy localization of two surface electromyographic (sEMG) signals in the time–frequency domain, i.e., the CWT coscalogram, was adopted for the first time to characterize muscular co-contraction activity. A CWT-based denoising procedure was applied for removing noise from the sEMG signals. Algorithm performances were checked on synthetic and real sEMG signals, stratified for signal-to-noise ratio (SNR), and then validated against an approach based on the acknowledged double-threshold statistical algorithm (DT). Results: The CWT approach provided an accurate prediction of co-contraction timing in simulated and real datasets, minimally affected by SNR variability. The novel contribution consisted of providing the frequency values of each muscle co-contraction detected in the time domain, allowing us to reveal a wide variability in the frequency content between subjects and within stride. Conclusions: The CWT approach represents a relevant improvement over state-of-the-art approaches that provide only a numerical co-contraction index or, at best, dynamic information in the time domain. The robustness of the methodology and the physiological reliability of the experimental results support the suitability of this approach for clinical applications. Full article

(This article belongs to the Special Issue Electromyography Signal Acquisition and Processing for Movement Analysis)

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10 pages, 5350 KiB

Open AccessArticle

The Influence of the sEMG Amplitude Estimation Technique on the EMG–Force Relationship

by Simone Ranaldi, Giovanni Corvini, Cristiano De Marchis and Silvia Conforto

Sensors 2022, 22(11), 3972; https://doi.org/10.3390/s22113972 - 24 May 2022

Cited by 6 | Viewed by 2578

Abstract

The estimation of the sEMG–force relationship is an open problem in the scientific literature; current methods show different limitations and can achieve good performance only on limited scenarios, failing to identify a general solution to the optimization of this kind of analysis. In this work, this relationship has been estimated on two different datasets related to isometric force-tracking experiments by calculating the sEMG amplitude using different fixed-time constant moving-window filters, as well as an adaptive time-varying algorithm. Results show how the adaptive methods might be the most appropriate choice for the estimation of the correlation between the sEMG signal and the force time course. Moreover, the comparison between adaptive and standard filters highlights how the time constants exploited in the estimation strategy is not the only influence factor on this kind of analysis; a time-varying approach is able to constantly capture more information with respect to fixed stationary approaches with comparable window lengths. Full article

(This article belongs to the Special Issue Electromyography Signal Acquisition and Processing for Movement Analysis)

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17 pages, 1061 KiB

Open AccessArticle

Machine Learning for Detection of Muscular Activity from Surface EMG Signals

by Francesco Di Nardo, Antonio Nocera, Alessandro Cucchiarelli, Sandro Fioretti and Christian Morbidoni

Sensors 2022, 22(9), 3393; https://doi.org/10.3390/s22093393 - 28 Apr 2022

Cited by 12 | Viewed by 4852

Abstract

Background: Muscular-activity timing is useful information that is extractable from surface EMG signals (sEMG). However, a reference method is not available yet. The aim of this study is to investigate the reliability of a novel machine-learning-based approach (DEMANN) in detecting the onset/offset timing of muscle activation from sEMG signals. Methods: A dataset of 2880 simulated sEMG signals, stratified for signal-to-noise ratio (SNR) and time support, was generated to train a hidden single-layer fully-connected neural network. DEMANN’s performance was evaluated on simulated sEMG signals and two different datasets of real sEMG signals. DEMANN was validated against different reference algorithms, including the acknowledged double-threshold statistical algorithm (DT). Results: DEMANN provided a reliable prediction of muscle onset/offset in simulated and real sEMG signals, being minimally affected by SNR variability. When directly compared with state-of-the-art algorithms, DEMANN introduced relevant improvements in prediction performances. Conclusions: These outcomes support DEMANN’s reliability in assessing onset/offset events in different motor tasks and the condition of signal quality (different SNR), improving reference-algorithm performances. Unlike other works, DEMANN’s adopts a machine learning approach where a neural network is trained by only simulated sEMG signals, avoiding the possible complications and costs associated with a typical experimental procedure, making this approach suitable to clinical practice. Full article

(This article belongs to the Special Issue Electromyography Signal Acquisition and Processing for Movement Analysis)

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16 pages, 3031 KiB

Open AccessArticle

Trunk Muscle Coactivation in People with and without Low Back Pain during Fatiguing Frequency-Dependent Lifting Activities

by Tiwana Varrecchia, Silvia Conforto, Alessandro Marco De Nunzio, Francesco Draicchio, Deborah Falla and Alberto Ranavolo

Sensors 2022, 22(4), 1417; https://doi.org/10.3390/s22041417 - 12 Feb 2022

Cited by 13 | Viewed by 8114

Abstract

Lifting tasks are manual material-handling activities and are commonly associated with work-related low back disorders. Instrument-based assessment tools are used to quantitatively assess the biomechanical risk associated with lifting activities. This study aims at highlighting different motor strategies in people with and without low back pain (LBP) during fatiguing frequency-dependent lifting tasks by using parameters of muscle coactivation. A total of 15 healthy controls (HC) and eight people with LBP performed three lifting tasks with a progressively increasing lifting index (LI), each lasting 15 min. Bilaterally erector spinae longissimus (ESL) activity and rectus abdominis superior (RAS) were recorded using bipolar surface electromyography systems (sEMG), and the time-varying multi-muscle coactivation function (TMCf) was computed. The TMCf can significantly discriminate each pair of LI and it is higher in LBP than HC. Collectively, our findings suggest that it is possible to identify different motor strategies between people with and without LBP. The main finding shows that LBP, to counteract pain, coactivates the trunk muscles more than HC, thereby adopting a strategy that is stiffer and more fatiguing. Full article

(This article belongs to the Special Issue Electromyography Signal Acquisition and Processing for Movement Analysis)

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15 pages, 1731 KiB

Open AccessArticle

Modified Functional Reach Test: Upper-Body Kinematics and Muscular Activity in Chronic Stroke Survivors

by Giorgia Marchesi, Giulia Ballardini, Laura Barone, Psiche Giannoni, Carmelo Lentino, Alice De Luca and Maura Casadio

Sensors 2022, 22(1), 230; https://doi.org/10.3390/s22010230 - 29 Dec 2021

Cited by 19 | Viewed by 4768

Abstract

Effective control of trunk muscles is fundamental to perform most daily activities. Stroke affects this ability also when sitting, and the Modified Functional Reach Test is a simple clinical method to evaluate sitting balance. We characterize the upper body kinematics and muscular activity during this test. Fifteen chronic stroke survivors performed twice, in separate sessions, three repetitions of the test in forward and lateral directions with their ipsilesional arm. We focused our analysis on muscles of the trunk and of the contralesional, not moving, arm. The bilateral activations of latissimi dorsi, trapezii transversalis and oblique externus abdominis were left/right asymmetric, for both test directions, except for the obliquus externus abdominis in the frontal reaching. Stroke survivors had difficulty deactivating the contralesional muscles at the end of each trial, especially the trapezii trasversalis in the lateral direction. The contralesional, non-moving arm had muscular activations modulated according to the movement phases of the moving arm. Repeating the task led to better performance in terms of reaching distance, supported by an increased activation of the trunk muscles. The reaching distance correlated negatively with the time-up-and-go test score. Full article

(This article belongs to the Special Issue Electromyography Signal Acquisition and Processing for Movement Analysis)

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12 pages, 898 KiB

Open AccessArticle

Validation of Visually Identified Muscle Potentials during Human Sleep Using High Frequency/Low Frequency Spectral Power Ratios

by Mo H. Modarres, Jonathan E. Elliott, Kristianna B. Weymann, Dennis Pleshakov, Donald L. Bliwise and Miranda M. Lim

Sensors 2022, 22(1), 55; https://doi.org/10.3390/s22010055 - 22 Dec 2021

Viewed by 2708

Abstract

Surface electromyography (EMG), typically recorded from muscle groups such as the mentalis (chin/mentum) and anterior tibialis (lower leg/crus), is often performed in human subjects undergoing overnight polysomnography. Such signals have great importance, not only in aiding in the definitions of normal sleep stages, but also in defining certain disease states with abnormal EMG activity during rapid eye movement (REM) sleep, e.g., REM sleep behavior disorder and parkinsonism. Gold standard approaches to evaluation of such EMG signals in the clinical realm are typically qualitative, and therefore burdensome and subject to individual interpretation. We originally developed a digitized, signal processing method using the ratio of high frequency to low frequency spectral power and validated this method against expert human scorer interpretation of transient muscle activation of the EMG signal. Herein, we further refine and validate our initial approach, applying this to EMG activity across 1,618,842 s of polysomnography recorded REM sleep acquired from 461 human participants. These data demonstrate a significant association between visual interpretation and the spectrally processed signals, indicating a highly accurate approach to detecting and quantifying abnormally high levels of EMG activity during REM sleep. Accordingly, our automated approach to EMG quantification during human sleep recording is practical, feasible, and may provide a much-needed clinical tool for the screening of REM sleep behavior disorder and parkinsonism. Full article

(This article belongs to the Special Issue Electromyography Signal Acquisition and Processing for Movement Analysis)

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17 pages, 5538 KiB

Open AccessArticle

A Coupled Piezoelectric Sensor for MMG-Based Human-Machine Interfaces

by Mateusz Szumilas, Michał Władziński and Krzysztof Wildner

Sensors 2021, 21(24), 8380; https://doi.org/10.3390/s21248380 - 15 Dec 2021

Cited by 9 | Viewed by 3699

Abstract

Mechanomyography (MMG) is a technique of recording muscles activity that may be considered a suitable choice for human–machine interfaces (HMI). The design of sensors used for MMG and their spatial distribution are among the deciding factors behind their successful implementation to HMI. We present a new design of a MMG sensor, which consists of two coupled piezoelectric discs in a single housing. The sensor’s functionality was verified in two experimental setups related to typical MMG applications: an estimation of the force/MMG relationship under static conditions and a neural network-based gesture classification. The results showed exponential relationships between acquired MMG and exerted force (for up to 60% of the maximal voluntary contraction) alongside good classification accuracy (94.3%) of eight hand motions based on MMG from a single-site acquisition at the forearm. The simplification of the MMG-based HMI interface in terms of spatial arrangement is rendered possible with the designed sensor. Full article

(This article belongs to the Special Issue Electromyography Signal Acquisition and Processing for Movement Analysis)

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13 pages, 1602 KiB

Open AccessArticle

Does the Score on the MRC Strength Scale Reflect Instrumented Measures of Maximal Torque and Muscle Activity in Post-Stroke Survivors?

by Pawel Kiper, Daniele Rimini, Deborah Falla, Alfonc Baba, Sebastian Rutkowski, Lorenza Maistrello and Andrea Turolla

Sensors 2021, 21(24), 8175; https://doi.org/10.3390/s21248175 - 7 Dec 2021

Cited by 10 | Viewed by 7737

Abstract

It remains unknown whether variation of scores on the Medical Research Council (MRC) scale for muscle strength is associated with operator-independent techniques: dynamometry and surface electromyography (sEMG). This study aimed to evaluate whether the scores of the MRC strength scale are associated with instrumented measures of torque and muscle activity in post-stroke survivors with severe hemiparesis both before and after an intervention. Patients affected by a first ischemic or hemorrhagic stroke within 6 months before enrollment and with complete paresis were included in the study. The pre- and post-treatment assessments included the MRC strength scale, sEMG, and dynamometry assessment of the triceps brachii (TB) and biceps brachii (BB) as measures of maximal elbow extension and flexion torque, respectively. Proprioceptive-based training was used as a treatment model, which consisted of multidirectional exercises with verbal feedback. Each treatment session lasted 1 h/day, 5 days a week for a total 15 sessions. Nineteen individuals with stroke participated in the study. A significant correlation between outcome measures for the BB (MRC and sEMG p = 0.0177, ρ = 0.601; MRC and torque p = 0.0001, ρ = 0.867) and TB (MRC and sEMG p = 0.0026, ρ = 0.717; MRC and torque p = 0.0001, ρ = 0.873) were observed post intervention. Regression models revealed a relationship between the MRC score and sEMG and torque measures for both the TB and BB. The results confirmed that variation on the MRC strength scale is associated with variation in sEMG and torque measures, especially post intervention. The regression model showed a causal relationship between MRC scale scores, sEMG, and torque assessments. Full article

(This article belongs to the Special Issue Electromyography Signal Acquisition and Processing for Movement Analysis)

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16 pages, 926 KiB

Open AccessArticle

Muscle Synergies and Clinical Outcome Measures Describe Different Factors of Upper Limb Motor Function in Stroke Survivors Undergoing Rehabilitation in a Virtual Reality Environment

by Lorenza Maistrello, Daniele Rimini, Vincent C. K. Cheung, Giorgia Pregnolato and Andrea Turolla

Sensors 2021, 21(23), 8002; https://doi.org/10.3390/s21238002 - 30 Nov 2021

Cited by 11 | Viewed by 3296

Abstract

Recent studies have investigated muscle synergies as biomarkers for stroke, but it remains controversial if muscle synergies and clinical observation convey the same information on motor impairment. We aim to identify whether muscle synergies and clinical scales convey the same information or not. Post-stroke patients were administered an upper limb treatment. Before (T0) and after (T1) treatment, we assessed motor performance with clinical scales and motor output with EMG-derived muscle synergies. We implemented an exploratory factor analysis (EFA) and a confirmatory factor analysis (CFA) to identify the underlying relationships among all variables, at T0 and T1, and a general linear regression model to infer any relationships between the similarity between the affected and unaffected synergies (Median-sp) and clinical outcomes at T0. Clinical variables improved with rehabilitation whereas muscle-synergy parameters did not show any significant change. EFA and CFA showed that clinical variables and muscle-synergy parameters (except Median-sp) were grouped into different factors. Regression model showed that Median-sp could be well predicted by clinical scales. The information underlying clinical scales and muscle synergies are therefore different. However, clinical scales well predicted the similarity between the affected and unaffected synergies. Our results may have implications on personalizing rehabilitation protocols. Full article

(This article belongs to the Special Issue Electromyography Signal Acquisition and Processing for Movement Analysis)

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20 pages, 3140 KiB

Open AccessArticle

Evaluation of Muscle Function by Means of a Muscle-Specific and a Global Index

by Samanta Rosati, Marco Ghislieri, Gregorio Dotti, Daniele Fortunato, Valentina Agostini, Marco Knaflitz and Gabriella Balestra

Sensors 2021, 21(21), 7186; https://doi.org/10.3390/s21217186 - 29 Oct 2021

Cited by 4 | Viewed by 19783

Abstract

Gait analysis applications in clinics are still uncommon, for three main reasons: (1) the considerable time needed to prepare the subject for the examination; (2) the lack of user-independent tools; (3) the large variability of muscle activation patterns observed in healthy and pathological subjects. Numerical indices quantifying the muscle coordination of a subject could enable clinicians to identify patterns that deviate from those of a reference population and to follow the progress of the subject after surgery or completing a rehabilitation program. In this work, we present two user-independent indices. First, a muscle-specific index (MFI) that quantifies the similarity of the activation pattern of a muscle of a specific subject with that of a reference population. Second, a global index (GFI) that provides a score of the overall activation of a muscle set. These two indices were tested on two groups of healthy and pathological children with encouraging results. Hence, the two indices will allow clinicians to assess the muscle activation, identifying muscles showing an abnormal activation pattern, and associate a functional score to every single muscle as well as to the entire muscle set. These opportunities could contribute to facilitating the diffusion of surface EMG analysis in clinics. Full article

(This article belongs to the Special Issue Electromyography Signal Acquisition and Processing for Movement Analysis)

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15 pages, 2347 KiB

Open AccessArticle

An Algorithm for Choosing the Optimal Number of Muscle Synergies during Walking

by Riccardo Ballarini, Marco Ghislieri, Marco Knaflitz and Valentina Agostini

Sensors 2021, 21(10), 3311; https://doi.org/10.3390/s21103311 - 11 May 2021

Cited by 12 | Viewed by 3355

Abstract

In motor control studies, the 90% thresholding of variance accounted for (VAF) is the classical way of selecting the number of muscle synergies expressed during a motor task. However, the adoption of an arbitrary cut-off has evident drawbacks. The aim of this work is to describe and validate an algorithm for choosing the optimal number of muscle synergies (ChoOSyn), which can overcome the limitations of VAF-based methods. The proposed algorithm is built considering the following principles: (1) muscle synergies should be highly consistent during the various motor task epochs (i.e., remaining stable in time), (2) muscle synergies should constitute a base with low intra-level similarity (i.e., to obtain information-rich synergies, avoiding redundancy). The algorithm performances were evaluated against traditional approaches (threshold-VAF at 90% and 95%, elbow-VAF and plateau-VAF), using both a simulated dataset and a real dataset of 20 subjects. The performance evaluation was carried out by analyzing muscle synergies extracted from surface electromyographic (sEMG) signals collected during walking tasks lasting 5 min. On the simulated dataset, ChoOSyn showed comparable performances compared to VAF-based methods, while, in the real dataset, it clearly outperformed the other methods, in terms of the fraction of correct classifications, mean error (ME), and root mean square error (RMSE). The proposed approach may be beneficial to standardize the selection of the number of muscle synergies between different research laboratories, independent of arbitrary thresholds. Full article

(This article belongs to the Special Issue Electromyography Signal Acquisition and Processing for Movement Analysis)

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