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4 pages, 188 KiB

Open AccessEditorial

On the Applications of EMG Sensors and Signals

by Ernest N. Kamavuako

Sensors 2022, 22(20), 7966; https://doi.org/10.3390/s22207966 - 19 Oct 2022

Cited by 6 | Viewed by 3646

Abstract

The ability to execute limb motions derives from composite command signals (or efferent signals) that stem from the central nervous system through the highway of the spinal cord and peripheral nerves to the muscles that drive the joints [...] Full article

(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)

Research

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16 pages, 2478 KiB

Open AccessArticle

Directed Functional Coordination Analysis of Swallowing Muscles in Healthy and Dysphagic Subjects by Surface Electromyography

by Yiyao Ye-Lin, Gema Prats-Boluda, Marina Galiano-Botella, Sebastian Roldan-Vasco, Andres Orozco-Duque and Javier Garcia-Casado

Sensors 2022, 22(12), 4513; https://doi.org/10.3390/s22124513 - 15 Jun 2022

Cited by 8 | Viewed by 3270

Abstract

Swallowing is a complex sequence of highly regulated and coordinated skeletal and smooth muscle activity. Previous studies have attempted to determine the temporal relationship between the muscles to establish the activation sequence pattern, assessing functional muscle coordination with cross-correlation or coherence, which is [...] Read more.

Swallowing is a complex sequence of highly regulated and coordinated skeletal and smooth muscle activity. Previous studies have attempted to determine the temporal relationship between the muscles to establish the activation sequence pattern, assessing functional muscle coordination with cross-correlation or coherence, which is seriously impaired by volume conduction. In the present work, we used conditional Granger causality from surface electromyography signals to analyse the directed functional coordination between different swallowing muscles in both healthy and dysphagic subjects ingesting saliva, water, and yoghurt boluses. In healthy individuals, both bilateral and ipsilateral muscles showed higher coupling strength than contralateral muscles. We also found a dominant downward direction in ipsilateral supra and infrahyoid muscles. In dysphagic subjects, we found a significantly higher right-to-left infrahyoid, right ipsilateral infra-to-suprahyoid, and left ipsilateral supra-to-infrahyoid interactions, in addition to significant differences in the left ipsilateral muscles between bolus types. Our results suggest that the functional coordination analysis of swallowing muscles contains relevant information on the swallowing process and possible dysfunctions associated with dysphagia, indicating that it could potentially be used to assess the progress of the disease or the effectiveness of rehabilitation therapies. Full article

(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)

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25 pages, 4380 KiB

Open AccessArticle

Applying Machine Learning to Finger Movements Using Electromyography and Visualization in Opensim

by Jose Amezquita-Garcia, Miguel Bravo-Zanoguera, Felix F. Gonzalez-Navarro, Roberto Lopez-Avitia and M. A. Reyna

Sensors 2022, 22(10), 3737; https://doi.org/10.3390/s22103737 - 14 May 2022

Cited by 7 | Viewed by 3508

Abstract

Electromyographic signals have been used with low-degree-of-freedom prostheses, and recently with multifunctional prostheses. Currently, they are also being used as inputs in the human–computer interface that controls interaction through hand gestures. Although there is a gap between academic publications on the control of [...] Read more.

Electromyographic signals have been used with low-degree-of-freedom prostheses, and recently with multifunctional prostheses. Currently, they are also being used as inputs in the human–computer interface that controls interaction through hand gestures. Although there is a gap between academic publications on the control of an upper-limb prosthesis developed in laboratories and its service in the natural environment, there are attempts to achieve easier control using multiple muscle signals. This work contributes to this, using a database and biomechanical simulation software, both open access, to seek simplicity in the classifiers, anticipating their implementation in microcontrollers and their execution in real time. Fifteen predefined finger movements of the hand were identified using classic classifiers such as Bayes, linear and quadratic discriminant analysis. The idealized movements of the database were modeled with Opensim for visualization. Combinations of two preprocessing methods—the forward sequential selection method and the feature normalization method—were evaluated to increase the efficiency of these classifiers. The statistical methods of cross-validation, analysis of variance (ANOVA) and Duncan were used to validate the results. Furthermore, the classifier with the best recognition result was redesigned into a new feature space using the sparse matrix algorithm to improve it, and to determine which features can be eliminated without degrading the classification. The classifiers yielded promising results—the quadratic discriminant being the best, achieving an average recognition rate for each individual considered of 96.16%, and with 78.36% for the total sample group of the eight subjects, in an independent test dataset. The study ends with the visual analysis under Opensim of the classified movements, in which the usefulness of this simulation tool is appreciated by revealing the muscular participation, which can be useful during the design of a multifunctional prosthesis. Full article

(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)

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10 pages, 591 KiB

Open AccessArticle

The Effect of EMG Features on the Classification of Swallowing Events and the Estimation of Fluid Intake Volume

by Carlotta Malvuccio and Ernest N. Kamavuako

Sensors 2022, 22(9), 3380; https://doi.org/10.3390/s22093380 - 28 Apr 2022

Cited by 6 | Viewed by 2093

Abstract

Nowadays, society is experiencing an increase in the number of adults aged 65 and over, and it is projected that the older adult population will triple in the coming decades. As older adults are prone to becoming dehydrated, which can significantly impact healthcare [...] Read more.

Nowadays, society is experiencing an increase in the number of adults aged 65 and over, and it is projected that the older adult population will triple in the coming decades. As older adults are prone to becoming dehydrated, which can significantly impact healthcare costs and staff, it is necessary to advance healthcare technologies to cater to such needs. However, there has not been an extensive research effort to implement a device that can autonomously track fluid intake. In particular, the ability of surface electromyographic sensors (sEMG) to monitor fluid intake has not been investigated in depth. Our previous study demonstrated a reasonable classification and estimation ability of sEMG using four features. This study aimed to examine if classification and estimation could be potentiated by combining an optimal subset of features from a library of forty-six time and frequency-domain features extracted from the data recorded using eleven subjects. Results demonstrated a classification accuracy of 95.94 ± 2.76% and an f-score of 94.93 ± 3.51% in differentiating between liquid swallows from non-liquid swallowing events using five features only, and a volume estimation RMSE of 2.80 ± 1.22 mL per sip and an average estimation error of 15.43 ± 8.64% using two features only. These results are encouraging and prove that sEMG could be a potential candidate for monitoring fluid intake. Full article

(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)

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18 pages, 2670 KiB

Open AccessArticle

A New Labeling Approach for Proportional Electromyographic Control

by Annette Hagengruber, Ulrike Leipscher, Bjoern M. Eskofier and Jörn Vogel

Sensors 2022, 22(4), 1368; https://doi.org/10.3390/s22041368 - 10 Feb 2022

Cited by 5 | Viewed by 2727

Abstract

Different control strategies are available for human machine interfaces based on electromyography (EMG) to map voluntary muscle signals to control signals of a remote controlled device. Complex systems such as robots or multi-fingered hands require a natural commanding, which can be realized with [...] Read more.

Different control strategies are available for human machine interfaces based on electromyography (EMG) to map voluntary muscle signals to control signals of a remote controlled device. Complex systems such as robots or multi-fingered hands require a natural commanding, which can be realized with proportional and simultaneous control schemes. Machine learning approaches and methods based on regression are often used to realize the desired functionality. Training procedures often include the tracking of visual stimuli on a screen or additional sensors, such as cameras or force sensors, to create labels for decoder calibration. In certain scenarios, where ground truth, such as additional sensor data, can not be measured, e.g., with people suffering from physical disabilities, these methods come with the challenge of generating appropriate labels. We introduce a new approach that uses the EMG-feature stream recorded during a simple training procedure to generate continuous labels. The method avoids synchronization mismatches in the labels and has no need for additional sensor data. Furthermore, we investigated the influence of the transient phase of the muscle contraction when using the new labeling approach. For this purpose, we performed a user study involving 10 subjects performing online 2D goal-reaching and tracking tasks on a screen. In total, five different labeling methods were tested, including three variations of the new approach as well as methods based on binary labels, which served as a baseline. Results of the evaluation showed that the introduced labeling approach in combination with the transient phase leads to a proportional command that is more accurate than using only binary labels. In summary, this work presents a new labeling approach for proportional EMG control without the need of a complex training procedure or additional sensors. Full article

(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)

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14 pages, 2175 KiB

Open AccessArticle

Effect of Fixed and sEMG-Based Adaptive Shared Steering Control on Distracted Driver Behavior

by Zheng Wang, Satoshi Suga, Edric John Cruz Nacpil, Bo Yang and Kimihiko Nakano

Sensors 2021, 21(22), 7691; https://doi.org/10.3390/s21227691 - 19 Nov 2021

Cited by 7 | Viewed by 2975

Abstract

Driver distraction is a well-known cause for traffic collisions worldwide. Studies have indicated that shared steering control, which actively provides haptic guidance torque on the steering wheel, effectively improves the performance of distracted drivers. Recently, adaptive shared steering control based on the forearm [...] Read more.

Driver distraction is a well-known cause for traffic collisions worldwide. Studies have indicated that shared steering control, which actively provides haptic guidance torque on the steering wheel, effectively improves the performance of distracted drivers. Recently, adaptive shared steering control based on the forearm muscle activity of the driver has been developed, although its effect on distracted driver behavior remains unclear. To this end, a high-fidelity driving simulator experiment was conducted involving 18 participants performing double lane change tasks. The experimental conditions comprised two driver states: attentive and distracted. Under each condition, evaluations were performed on three types of haptic guidance: none (manual), fixed authority, and adaptive authority based on feedback from the forearm surface electromyography of the driver. Evaluation results indicated that, for both attentive and distracted drivers, haptic guidance with adaptive authority yielded lower driver workload and reduced lane departure risk than manual driving and fixed authority. Moreover, there was a tendency for distracted drivers to reduce grip strength on the steering wheel to follow the haptic guidance with fixed authority, resulting in a relatively shorter double lane change duration. Full article

(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)

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19 pages, 6488 KiB

Open AccessArticle

sEMG-Based Hand Posture Recognition Considering Electrode Shift, Feature Vectors, and Posture Groups

by Jongman Kim, Bummo Koo, Yejin Nam and Youngho Kim

Sensors 2021, 21(22), 7681; https://doi.org/10.3390/s21227681 - 18 Nov 2021

Cited by 9 | Viewed by 2592

Abstract

Surface electromyography (sEMG)-based gesture recognition systems provide the intuitive and accurate recognition of various gestures in human-computer interaction. In this study, an sEMG-based hand posture recognition algorithm was developed, considering three main problems: electrode shift, feature vectors, and posture groups. The sEMG signal [...] Read more.

Surface electromyography (sEMG)-based gesture recognition systems provide the intuitive and accurate recognition of various gestures in human-computer interaction. In this study, an sEMG-based hand posture recognition algorithm was developed, considering three main problems: electrode shift, feature vectors, and posture groups. The sEMG signal was measured using an armband sensor with the electrode shift. An artificial neural network classifier was trained using 21 feature vectors for seven different posture groups. The inter-session and inter-feature Pearson correlation coefficients (PCCs) were calculated. The results indicate that the classification performance improved with the number of training sessions of the electrode shift. The number of sessions necessary for efficient training was four, and the feature vectors with a high inter-session PCC (r > 0.7) exhibited high classification accuracy. Similarities between postures in a posture group decreased the classification accuracy. Our results indicate that the classification accuracy could be improved with the addition of more electrode shift training sessions and that the PCC is useful for selecting the feature vector. Furthermore, hand posture selection was as important as feature vector selection. These findings will help in optimizing the sEMG-based pattern recognition algorithm more easily and quickly. Full article

(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)

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15 pages, 1224 KiB

Open AccessArticle

Flexible Recruitments of Fundamental Muscle Synergies in the Trunk and Lower Limbs for Highly Variable Movements and Postures

by Hiroki Saito, Hikaru Yokoyama, Atsushi Sasaki, Tatsuya Kato and Kimitaka Nakazawa

Sensors 2021, 21(18), 6186; https://doi.org/10.3390/s21186186 - 15 Sep 2021

Cited by 12 | Viewed by 4360

Abstract

The extent to which muscle synergies represent the neural control of human behavior remains unknown. Here, we tested whether certain sets of muscle synergies that are fundamentally necessary across behaviors exist. We measured the electromyographic activities of 26 muscles, including bilateral trunk and [...] Read more.

The extent to which muscle synergies represent the neural control of human behavior remains unknown. Here, we tested whether certain sets of muscle synergies that are fundamentally necessary across behaviors exist. We measured the electromyographic activities of 26 muscles, including bilateral trunk and lower limb muscles, during 24 locomotion, dynamic and static stability tasks, and we extracted the muscle synergies using non-negative matrix factorization. Our results show that 13 muscle synergies that may have unique functional roles accounted for almost all 24 tasks by combinations of single and/or merging of synergies. Therefore, our results may support the notion of the low dimensionality in motor outputs, in which the central nervous system flexibly recruits fundamental muscle synergies to execute diverse human behaviors. Further studies are required to validate the neural representation of the fundamental components of muscle synergies. Full article

(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)

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16 pages, 7706 KiB

Open AccessArticle

Wheelchair-Mounted Upper Limb Robotic Exoskeleton with Adaptive Controller for Activities of Daily Living

by Bridget Schabron, Jaydip Desai and Yimesker Yihun

Sensors 2021, 21(17), 5738; https://doi.org/10.3390/s21175738 - 26 Aug 2021

Cited by 14 | Viewed by 4400

Abstract

Neuro-muscular disorders and diseases such as cerebral palsy and Duchenne Muscular Dystrophy can severely limit a person’s ability to perform activities of daily living (ADL). Exoskeletons can provide an active or passive support solution to assist these groups of people to perform ADL. [...] Read more.

Neuro-muscular disorders and diseases such as cerebral palsy and Duchenne Muscular Dystrophy can severely limit a person’s ability to perform activities of daily living (ADL). Exoskeletons can provide an active or passive support solution to assist these groups of people to perform ADL. This study presents an artificial neural network-trained adaptive controller mechanism that uses surface electromyography (sEMG) signals from the human forearm to detect hand gestures and navigate an in-house-built wheelchair-mounted upper limb robotic exoskeleton based on the user’s intent while ensuring safety. To achieve the desired position of the exoskeleton based on human intent, 10 hand gestures were recorded from 8 participants without upper limb movement disabilities. Participants were tasked to perform water bottle pick and place activities while using the exoskeleton, and sEMG signals were collected from the forearm and processed through root mean square, median filter, and mean feature extractors prior to training a scaled conjugate gradient backpropagation artificial neural network. The trained network achieved an average of more than 93% accuracy, while all 8 participants who did not have any prior experience of using an exoskeleton were successfully able to perform the task in less than 20 s using the proposed artificial neural network-trained adaptive controller mechanism. These results are significant and promising thus could be tested on people with muscular dystrophy and neuro-degenerative diseases. Full article

(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)

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11 pages, 2070 KiB

Open AccessArticle

Affordable Embroidered EMG Electrodes for Myoelectric Control of Prostheses: A Pilot Study

by Ernest N. Kamavuako, Mitchell Brown, Xinqi Bao, Ines Chihi, Samuel Pitou and Matthew Howard

Sensors 2021, 21(15), 5245; https://doi.org/10.3390/s21155245 - 3 Aug 2021

Cited by 11 | Viewed by 4032

Abstract

Commercial myoelectric prostheses are costly to purchase and maintain, making their provision challenging for developing countries. Recent research indicates that embroidered EMG electrodes may provide a more affordable alternative to the sensors used in current prostheses. This pilot study investigates the usability of [...] Read more.

Commercial myoelectric prostheses are costly to purchase and maintain, making their provision challenging for developing countries. Recent research indicates that embroidered EMG electrodes may provide a more affordable alternative to the sensors used in current prostheses. This pilot study investigates the usability of such electrodes for myoelectric control by comparing online and offline performance against conventional gel electrodes. Offline performance is evaluated through the classification of nine different hand and wrist gestures. Online performance is assessed with a crossover two-degree-of-freedom real-time experiment using Fitts’ Law. Two performance metrics (Throughput and Completion Rate) are used to quantify usability. The mean classification accuracy of the nine gestures is approximately 98% for subject-specific models trained on both gel and embroidered electrode offline data from individual subjects, and 97% and 96% for general models trained on gel and embroidered offline data, respectively, from all subjects. Throughput (0.3 bits/s) and completion rate (95–97%) are similar in the online test. Results indicate that embroidered electrodes can achieve similar performance to gel electrodes paving the way for low-cost myoelectric prostheses. Full article

(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)

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25 pages, 3512 KiB

Open AccessArticle

A Statistical Method for Exploratory Data Analysis Based on 2D and 3D Area under Curve Diagrams: Parkinson’s Disease Investigation

by Olga Sergeevna Sushkova, Alexei Alexandrovich Morozov, Alexandra Vasilievna Gabova, Alexei Vyacheslavovich Karabanov and Sergey Nikolaevich Illarioshkin

Sensors 2021, 21(14), 4700; https://doi.org/10.3390/s21144700 - 9 Jul 2021

Cited by 13 | Viewed by 6420

Abstract

A statistical method for exploratory data analysis based on 2D and 3D area under curve (AUC) diagrams was developed. The method was designed to analyze electroencephalogram (EEG), electromyogram (EMG), and tremorogram data collected from patients with Parkinson’s disease. The idea of the method [...] Read more.

A statistical method for exploratory data analysis based on 2D and 3D area under curve (AUC) diagrams was developed. The method was designed to analyze electroencephalogram (EEG), electromyogram (EMG), and tremorogram data collected from patients with Parkinson’s disease. The idea of the method of wave train electrical activity analysis is that we consider the biomedical signal as a combination of the wave trains. The wave train is the increase in the power spectral density of the signal localized in time, frequency, and space. We detect the wave trains as the local maxima in the wavelet spectrograms. We do not consider wave trains as a special kind of signal. The wave train analysis method is different from standard signal analysis methods such as Fourier analysis and wavelet analysis in the following way. Existing methods for analyzing EEG, EMG, and tremor signals, such as wavelet analysis, focus on local time–frequency changes in the signal and therefore do not reveal the generalized properties of the signal. Other methods such as standard Fourier analysis ignore the local time–frequency changes in the characteristics of the signal and, consequently, lose a large amount of information that existed in the signal. The method of wave train electrical activity analysis resolves the contradiction between these two approaches because it addresses the generalized characteristics of the biomedical signal based on local time–frequency changes in the signal. We investigate the following wave train parameters: wave train central frequency, wave train maximal power spectral density, wave train duration in periods, and wave train bandwidth. We have developed special graphical diagrams, named AUC diagrams, to determine what wave trains are characteristic of neurodegenerative diseases. In this paper, we consider the following types of AUC diagrams: 2D and 3D diagrams. The technique of working with AUC diagrams is illustrated by examples of analysis of EMG in patients with Parkinson’s disease and healthy volunteers. It is demonstrated that new regularities useful for the high-accuracy diagnosis of Parkinson’s disease can be revealed using the method of analyzing the wave train electrical activity and AUC diagrams. Full article

(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)

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15 pages, 2378 KiB

Open AccessArticle

A Novel Muscle Synergy Extraction Method Used for Motor Function Evaluation of Stroke Patients: A Pilot Study

by Yehao Ma, Changcheng Shi, Jialin Xu, Sijia Ye, Huilin Zhou and Guokun Zuo

Sensors 2021, 21(11), 3833; https://doi.org/10.3390/s21113833 - 1 Jun 2021

Cited by 15 | Viewed by 3513

Abstract

In this paper, we present a novel muscle synergy extraction method based on multivariate curve resolution–alternating least squares (MCR-ALS) to overcome the limitation of the nonnegative matrix factorization (NMF) method for extracting non-sparse muscle synergy, and we study its potential application for evaluating [...] Read more.

In this paper, we present a novel muscle synergy extraction method based on multivariate curve resolution–alternating least squares (MCR-ALS) to overcome the limitation of the nonnegative matrix factorization (NMF) method for extracting non-sparse muscle synergy, and we study its potential application for evaluating motor function of stroke survivors. Nonnegative matrix factorization (NMF) is the most widely used method for muscle synergy extraction. However, NMF is susceptible to components’ sparseness and usually provides inferior reliability, which significantly limits the promotion of muscle synergy. In this study, MCR-ALS was employed to extract muscle synergy from electromyography (EMG) data. Its performance was compared with two other matrix factorization algorithms, NMF and self-modeling mixture analysis (SMMA). Simulated data sets were utilized to explore the influences of the sparseness and noise on the extracted synergies. As a result, the synergies estimated by MCR-ALS were the most similar to true synergies as compared with SMMA and NMF. MCR-ALS was used to analyze the muscle synergy characteristics of upper limb movements performed by healthy (n = 11) and stroke (n = 5) subjects. The repeatability and intra-subject consistency were used to evaluate the performance of MCR-ALS. As a result, MCR-ALS provided much higher repeatability and intra-subject consistency as compared with NMF, which were important for the reliability of the motor function evaluation. The stroke subjects had lower intra-subject consistency and seemingly had more synergies as compared with the healthy subjects. Thus, MCR-ALS is a promising muscle synergy analysis method for motor function evaluation of stroke patients. Full article

(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)

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14 pages, 1181 KiB

Open AccessArticle

Electromyographic Assessment of the Efficacy of Deep Dry Needling versus the Ischemic Compression Technique in Gastrocnemius of Medium-Distance Triathletes

by María Benito-de-Pedro, César Calvo-Lobo, Daniel López-López, Ana Isabel Benito-de-Pedro, Carlos Romero-Morales, Marta San-Antolín, Davinia Vicente-Campos and David Rodríguez-Sanz

Sensors 2021, 21(9), 2906; https://doi.org/10.3390/s21092906 - 21 Apr 2021

Cited by 4 | Viewed by 3230

Abstract

Several studies have shown that gastrocnemius is frequently injured in triathletes. The causes of these injuries are similar to those that cause the appearance of the myofascial pain syndrome (MPS). The ischemic compression technique (ICT) and deep dry needling (DDN) are considered two [...] Read more.

Several studies have shown that gastrocnemius is frequently injured in triathletes. The causes of these injuries are similar to those that cause the appearance of the myofascial pain syndrome (MPS). The ischemic compression technique (ICT) and deep dry needling (DDN) are considered two of the main MPS treatment methods in latent myofascial trigger points (MTrPs). In this study superficial electromyographic (EMG) activity in lateral and medial gastrocnemius of triathletes with latent MTrPs was measured before and immediately after either DDN or ICT treatment. Taking into account superficial EMG activity of lateral and medial gastrocnemius, the immediate effectiveness in latent MTrPs of both DDN and ICT was compared. A total of 34 triathletes was randomly divided in two groups. The first and second groups (n = 17 in each group) underwent only one session of DDN and ICT, respectively. EMG measurement of gastrocnemius was assessed before and immediately after treatment. Statistically significant differences (p = 0.037) were shown for a reduction of superficial EMG measurements differences (%) of the experimental group (DDN) with respect to the intervention group (ICT) at a speed of 1 m/s immediately after both interventions, although not at speeds of 1.5 m/s or 2.5 m/s. A statistically significant linear regression prediction model was shown for EMG outcome measurement differences at V1 (speed of 1 m/s) which was only predicted for the treatment group (R² = 0.129; β = 8.054; F = 4.734; p = 0.037) showing a reduction of this difference under DDN treatment. DDN administration requires experience and excellent anatomical knowledge. According to our findings immediately after treatment of latent MTrPs, DDN could be advisable for triathletes who train at a speed lower than 1 m/s, while ICT could be a more advisable technique than DDN for training or competitions at speeds greater than 1.5 m/s. Full article

(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)

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15 pages, 1355 KiB

Open AccessArticle

Noninvasive Assessment of Neuromechanical Coupling and Mechanical Efficiency of Parasternal Intercostal Muscle during Inspiratory Threshold Loading

by Manuel Lozano-García, Luis Estrada-Petrocelli, Abel Torres, Gerrard F. Rafferty, John Moxham, Caroline J. Jolley and Raimon Jané

Sensors 2021, 21(5), 1781; https://doi.org/10.3390/s21051781 - 4 Mar 2021

Cited by 6 | Viewed by 4428

Abstract

This study aims to investigate noninvasive indices of neuromechanical coupling (NMC) and mechanical efficiency (MEff) of parasternal intercostal muscles. Gold standard assessment of diaphragm NMC requires using invasive techniques, limiting the utility of this procedure. Noninvasive NMC indices of parasternal intercostal muscles can [...] Read more.

This study aims to investigate noninvasive indices of neuromechanical coupling (NMC) and mechanical efficiency (MEff) of parasternal intercostal muscles. Gold standard assessment of diaphragm NMC requires using invasive techniques, limiting the utility of this procedure. Noninvasive NMC indices of parasternal intercostal muscles can be calculated using surface mechanomyography (sMMG_para) and electromyography (sEMG_para). However, the use of sMMG_para as an inspiratory muscle mechanical output measure, and the relationships between sMMG_para, sEMG_para, and simultaneous invasive and noninvasive pressure measurements have not previously been evaluated. sEMG_para, sMMG_para, and both invasive and noninvasive measurements of pressures were recorded in twelve healthy subjects during an inspiratory loading protocol. The ratios of sMMG_para to sEMG_para, which provided muscle-specific noninvasive NMC indices of parasternal intercostal muscles, showed nonsignificant changes with increasing load, since the relationships between sMMG_para and sEMG_para were linear (R² = 0.85 (0.75–0.9)). The ratios of mouth pressure (P_mo) to sEMG_para and sMMG_para were also proposed as noninvasive indices of parasternal intercostal muscle NMC and MEff, respectively. These indices, similar to the analogous indices calculated using invasive transdiaphragmatic and esophageal pressures, showed nonsignificant changes during threshold loading, since the relationships between P_mo and both sEMG_para (R² = 0.84 (0.77–0.93)) and sMMG_para (R² = 0.89 (0.85–0.91)) were linear. The proposed noninvasive NMC and MEff indices of parasternal intercostal muscles may be of potential clinical value, particularly for the regular assessment of patients with disordered respiratory mechanics using noninvasive wearable and wireless devices. Full article

(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)

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16 pages, 3698 KiB

Open AccessArticle

Decoding of Ankle Joint Movements in Stroke Patients Using Surface Electromyography

by Afaq Noor, Asim Waris, Syed Omer Gilani, Amer Sohail Kashif, Mads Jochumsen, Javaid Iqbal and Imran Khan Niazi

Sensors 2021, 21(5), 1575; https://doi.org/10.3390/s21051575 - 24 Feb 2021

Cited by 5 | Viewed by 4386

Abstract

Stroke is a cerebrovascular disease (CVD), which results in hemiplegia, paralysis, or death. Conventionally, a stroke patient requires prolonged sessions with physical therapists for the recovery of motor function. Various home-based rehabilitative devices are also available for upper limbs and require minimal or [...] Read more.

Stroke is a cerebrovascular disease (CVD), which results in hemiplegia, paralysis, or death. Conventionally, a stroke patient requires prolonged sessions with physical therapists for the recovery of motor function. Various home-based rehabilitative devices are also available for upper limbs and require minimal or no assistance from a physiotherapist. However, there is no clinically proven device available for functional recovery of a lower limb. In this study, we explored the potential use of surface electromyography (sEMG) as a controlling mechanism for the development of a home-based lower limb rehabilitative device for stroke patients. In this experiment, three channels of sEMG were used to record data from 11 stroke patients while performing ankle joint movements. The movements were then decoded from the sEMG data and their correlation with the level of motor impairment was investigated. The impairment level was quantified using the Fugl-Meyer Assessment (FMA) scale. During the analysis, Hudgins time-domain features were extracted and classified using linear discriminant analysis (LDA) and artificial neural network (ANN). On average, 63.86% ± 4.3% and 67.1% ± 7.9% of the movements were accurately classified in an offline analysis by LDA and ANN, respectively. We found that in both classifiers, some motions outperformed others (p < 0.001 for LDA and p = 0.014 for ANN). The Spearman correlation (ρ) was calculated between the FMA scores and classification accuracies. The results indicate that there is a moderately positive correlation (ρ = 0.75 for LDA and ρ = 0.55 for ANN) between the two of them. The findings of this study suggest that a home-based EMG system can be developed to provide customized therapy for the improvement of functional lower limb motion in stroke patients. Full article

(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)

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23 pages, 2521 KiB

Open AccessArticle

Using a System-Based Monitoring Paradigm to Assess Fatigue during Submaximal Static Exercise of the Elbow Extensor Muscles

by Kaci E. Madden, Dragan Djurdjanovic and Ashish D. Deshpande

Sensors 2021, 21(4), 1024; https://doi.org/10.3390/s21041024 - 3 Feb 2021

Cited by 7 | Viewed by 3010

Abstract

Current methods for evaluating fatigue separately assess intramuscular changes in individual muscles from corresponding alterations in movement output. The purpose of this study is to investigate if a system-based monitoring paradigm, which quantifies how the dynamic relationship between the activity from multiple muscles [...] Read more.

Current methods for evaluating fatigue separately assess intramuscular changes in individual muscles from corresponding alterations in movement output. The purpose of this study is to investigate if a system-based monitoring paradigm, which quantifies how the dynamic relationship between the activity from multiple muscles and force changes over time, produces a viable metric for assessing fatigue. Improvements made to the paradigm to facilitate online fatigue assessment are also discussed. Eight participants performed a static elbow extension task until exhaustion, while surface electromyography (sEMG) and force data were recorded. A dynamic time-series model mapped instantaneous features extracted from sEMG signals of multiple synergistic muscles to extension force. A metric, called the Freshness Similarity Index (FSI), was calculated using statistical analysis of modeling errors to reveal time-dependent changes in the dynamic model indicative of performance degradation. The FSI revealed strong, significant within-individual associations with two well-accepted measures of fatigue, maximum voluntary contraction (MVC) force (

r_{r m} = - 0.86

) and ratings of perceived exertion (RPE) (

r_{r m} = 0.87

), substantiating the viability of a system-based monitoring paradigm for assessing fatigue. These findings provide the first direct and quantitative link between a system-based performance degradation metric and traditional measures of fatigue. Full article

(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)

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13 pages, 27044 KiB

Open AccessCommunication

Arduino-Based Myoelectric Control: Towards Longitudinal Study of Prosthesis Use

by Hancong Wu, Matthew Dyson and Kianoush Nazarpour

Sensors 2021, 21(3), 763; https://doi.org/10.3390/s21030763 - 24 Jan 2021

Cited by 30 | Viewed by 8899

Abstract

Understanding how upper-limb prostheses are used in daily life helps to improve the design and robustness of prosthesis control algorithms and prosthetic components. However, only a very small fraction of published research includes prosthesis use in community settings. The cost, limited battery life, [...] Read more.

Understanding how upper-limb prostheses are used in daily life helps to improve the design and robustness of prosthesis control algorithms and prosthetic components. However, only a very small fraction of published research includes prosthesis use in community settings. The cost, limited battery life, and poor generalisation may be the main reasons limiting the implementation of home-based applications. In this work, we introduce the design of a cost-effective Arduino-based myoelectric control system with wearable electromyogram (EMG) sensors. The design considerations focused on home studies, so the robustness, user-friendly control adjustments, and user supports were the main concerns. Three control algorithms, namely, direct control, abstract control, and linear discriminant analysis (LDA) classification, were implemented in the system. In this paper, we will share our design principles and report the robustness of the system in continuous operation in the laboratory. In addition, we will show a first real-time implementation of the abstract decoder for prosthesis control with an able-bodied participant. Full article

(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)

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20 pages, 1629 KiB

Open AccessArticle

Ablation Analysis to Select Wearable Sensors for Classifying Standing, Walking, and Running

by Sarah Gonzalez, Paul Stegall, Harvey Edwards, Leia Stirling and Ho Chit Siu

Sensors 2021, 21(1), 194; https://doi.org/10.3390/s21010194 - 30 Dec 2020

Cited by 11 | Viewed by 2940

Abstract

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was [...] Read more.

The field of human activity recognition (HAR) often utilizes wearable sensors and machine learning techniques in order to identify the actions of the subject. This paper considers the activity recognition of walking and running while using a support vector machine (SVM) that was trained on principal components derived from wearable sensor data. An ablation analysis is performed in order to select the subset of sensors that yield the highest classification accuracy. The paper also compares principal components across trials to inform the similarity of the trials. Five subjects were instructed to perform standing, walking, running, and sprinting on a self-paced treadmill, and the data were recorded while using surface electromyography sensors (sEMGs), inertial measurement units (IMUs), and force plates. When all of the sensors were included, the SVM had over 90% classification accuracy using only the first three principal components of the data with the classes of stand, walk, and run/sprint (combined run and sprint class). It was found that sensors that were placed only on the lower leg produce higher accuracies than sensors placed on the upper leg. There was a small decrease in accuracy when the force plates are ablated, but the difference may not be operationally relevant. Using only accelerometers without sEMGs was shown to decrease the accuracy of the SVM. Full article

(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)

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Review

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26 pages, 650 KiB

Open AccessReview

A Systematic Review of EMG Applications for the Characterization of Forearm and Hand Muscle Activity during Activities of Daily Living: Results, Challenges, and Open Issues

by Néstor J. Jarque-Bou, Joaquín L. Sancho-Bru and Margarita Vergara

Sensors 2021, 21(9), 3035; https://doi.org/10.3390/s21093035 - 26 Apr 2021

Cited by 47 | Viewed by 10007

Abstract

The role of the hand is crucial for the performance of activities of daily living, thereby ensuring a full and autonomous life. Its motion is controlled by a complex musculoskeletal system of approximately 38 muscles. Therefore, measuring and interpreting the muscle activation signals [...] Read more.

The role of the hand is crucial for the performance of activities of daily living, thereby ensuring a full and autonomous life. Its motion is controlled by a complex musculoskeletal system of approximately 38 muscles. Therefore, measuring and interpreting the muscle activation signals that drive hand motion is of great importance in many scientific domains, such as neuroscience, rehabilitation, physiotherapy, robotics, prosthetics, and biomechanics. Electromyography (EMG) can be used to carry out the neuromuscular characterization, but it is cumbersome because of the complexity of the musculoskeletal system of the forearm and hand. This paper reviews the main studies in which EMG has been applied to characterize the muscle activity of the forearm and hand during activities of daily living, with special attention to muscle synergies, which are thought to be used by the nervous system to simplify the control of the numerous muscles by actuating them in task-relevant subgroups. The state of the art of the current results are presented, which may help to guide and foster progress in many scientific domains. Furthermore, the most important challenges and open issues are identified in order to achieve a better understanding of human hand behavior, improve rehabilitation protocols, more intuitive control of prostheses, and more realistic biomechanical models. Full article

(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)

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32 pages, 1694 KiB

Open AccessReview

Control Strategies and Performance Assessment of Upper-Limb TMR Prostheses: A Review

by Federico Mereu, Francesca Leone, Cosimo Gentile, Francesca Cordella, Emanuele Gruppioni and Loredana Zollo

Sensors 2021, 21(6), 1953; https://doi.org/10.3390/s21061953 - 10 Mar 2021

Cited by 21 | Viewed by 5436

Abstract

The evolution of technological and surgical techniques has made it possible to obtain an even more intuitive control of multiple joints using advanced prosthetic systems. Targeted Muscle Reinnervation (TMR) is considered to be an innovative and relevant surgical technique for improving the prosthetic [...] Read more.

The evolution of technological and surgical techniques has made it possible to obtain an even more intuitive control of multiple joints using advanced prosthetic systems. Targeted Muscle Reinnervation (TMR) is considered to be an innovative and relevant surgical technique for improving the prosthetic control for people with different amputation levels of the limb. Indeed, TMR surgery makes it possible to obtain reinnervated areas that act as biological amplifiers of the motor control. On the technological side, a great deal of research has been conducted in order to evaluate various types of myoelectric prosthetic control strategies, whether direct control or pattern recognition-based control. In the literature, different control performance metrics, which have been evaluated on TMR subjects, have been introduced, but no accepted reference standard defines the better strategy for evaluating the prosthetic control. Indeed, the presence of several evaluation tests that are based on different metrics makes it difficult the definition of standard guidelines for comprehending the potentiality of the proposed control systems. Additionally, there is a lack of evidence about the comparison of different evaluation approaches or the presence of guidelines on the most suitable test to proceed for a TMR patients case study. Thus, this review aims at identifying these limitations by examining the several studies in the literature on TMR subjects, with different amputation levels, and proposing a standard method for evaluating the control performance metrics. Full article

(This article belongs to the Special Issue On the Applications of EMG Sensors and Signals)

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