Surface Electromyography Sensors Research Articles

Clinical Spasticity Assessment Assisted by Machine Learning Methods and Rule-Based Decision

The Modified Ashworth Scale (MAS) is commonly used to assess spasticity in clinics. The qualitative description of MAS has resulted in ambiguity during spasticity assessment. This work supports spasticity assessment by providing measurement data acquired from wireless wearable sensors, i.e., goniometers, myometers, and surface electromyography sensors. Based on in-depth discussions with consultant rehabilitation physicians, eight (8) kinematic, six (6) kinetic, and four (4) physiological features were extracted from the collected clinical data from fifty (50) subjects. These features were used to train and evaluate the conventional machine learning classifiers, including but not limited to Support Vector Machine (SVM) and Random Forest (RF). Subsequently, a spasticity classification approach combining the decision-making logic of the consultant rehabilitation physicians, SVM, and RF was developed. The empirical results on the unknown test set show that the proposed Logical-SVM-RF classifier outperforms each individual classifier, reporting an accuracy of 91% compared to 56-81% achieved by SVM and RF. A data-driven diagnosis decision contributing to interrater reliability is enabled via the availability of quantitative clinical data and a MAS prediction.

SEMG-Based Hand Gesture Recognition Using Binarized Neural Network.

Recently, human-machine interfaces (HMI) that make life convenient have been studied in many fields. In particular, a hand gesture recognition (HGR) system, which can be implemented as a wearable system, has the advantage that users can easily and intuitively control the device. Among the various sensors used in the HGR system, the surface electromyography (sEMG) sensor is independent of the acquisition environment, easy to wear, and requires a small amount of data. Focusing on these advantages, previous sEMG-based HGR systems used several sensors or complex deep-learning algorithms to achieve high classification accuracy. However, systems that use multiple sensors are bulky, and embedded platforms with complex deep-learning algorithms are difficult to implement. To overcome these limitations, we propose an HGR system using a binarized neural network (BNN), a lightweight convolutional neural network (CNN), with one dry-type sEMG sensor, which is implemented on a field-programmable gate array (FPGA). The proposed HGR system classifies nine dynamic gestures that can be useful in real life rather than static gestures that can be classified relatively easily. Raw sEMG data collected from a dynamic gesture are converted into a spectrogram with information in the time-frequency domain and transferred to the classifier. As a result, the proposed HGR system achieved 95.4% classification accuracy, with a computation time of 14.1 ms and a power consumption of 91.81 mW.

A robust HD-sEMG sensor suitable for convenient acquisition of muscle activity in clinical post-stroke dysphagia

Objective. A flexible high-density surface electromyography (HD-sEMG) sensor combined with an adaptive algorithm was used to collect and analyze the swallowing activities of patients with Post-stroke dysphagia. Approach. The electrode frame, modified electrode, and bonded substrate of the sensor were fabricated using a flexible printed circuit process, controlled drop coating, and molding, respectively. The adaptation algorithm was achieved by using Laplace and Teager-Kaiser energy operators to extract active segments, a cross-correlation coefficient matrix (CCCM) to evaluate synergy, and multi-frame real-time dynamic root mean square (RMS) to visualize spatiotemporal information to screen lesions and level of dysphagia. Finally, support vector machines (SVM) were adopted to explore the classification accuracy of sex, age, and lesion location with small sample sizes. Main results. The sensor not only has a basic low contact impedance (0.262 kΩ) and high signal-to-noise ratio (37.284 ± 1.088 dB) but also achieves other characteristics suitable for clinical applications, such as flexibility (747.67 kPa) and durability (1000 times) balance, simple operation (including initial, repeated, and replacement use), and low cost ($ 15.2). The three conclusions are as follows. CCCM can be used as a criterion for judging the unbalanced muscle region of the patient’s neck and can accurately locate unbalanced muscles. The RMS cloud map provides the time consumption, swallowing times, and unbalanced areas. When the lesion location involves the left and right hemispheres simultaneously, it can be used as an evidence of relatively severely unbalanced areas. The classification accuracy of SVM in terms of sex, age, and lesion location was as high as 100%. Significance. The HD-sEMG sensor in this study and the adaptation algorithm will contribute to the establishment of a larger-scale database in the future to establish more detailed and accurate quantitative standards, which will be the basis for developing more optimized screening mechanisms and rehabilitation assessment methods.

Lower-limb Non-parametric Functional Muscle Network: Test-retest Reliability Analysis.

Functional muscle network analysis has attracted a great deal of interest in recent years, promising high sensitivity to changes of intermuscular synchronicity, studied mostly for healthy subjects and recently for patients living with neurological conditions (e.g., those caused by stroke). Despite the promising results, the between- and within-session reliability of the functional muscle network measures are yet to be established. Here, for the first time, we question and evaluate the test-retest reliability of non-parametric lower-limb functional muscle networks for controlled and lightly-controlled tasks, i.e., sit-to-stand, and over-the-ground walking, respectively, in healthy subjects. Fifteen subjects (eight females) were included over two sessions on two different days. The muscle activity was recorded using 14 surface electromyography (sEMG) sensors. The intraclass correlation coefficient (ICC) of the within-session and between-session trials was quantified for the various network metrics, including degree and weighted clustering coefficient. In order to compare with common classical sEMG measures, the reliabilities of the root mean square (RMS) of sEMG and the median frequency (MDF) of sEMG were also calculated. The ICC analysis revealed superior between-session reliability for muscle networks, with statistically significant differences when compared to classic measures. This paper proposed that the topographical metrics generated from functional muscle network can be reliably used for multi-session observations securing high reliability for quantifying the distribution of synergistic intermuscular synchronicities of both controlled and lightly controlled lower limb tasks. In addition, the low number of sessions required by the topographical network metrics to reach reliable measurements indicates the potential as biomarkers during rehabilitation.

Children's strategies in drop-landing.

Landing is a critical motor skill included in many activities performed in the natural environment by young children. Yet, landing is critically relevance to ensure proper stability and reduce injury. Furthermore, landing is an integral part of many fundamental motor skills which have been linked to greater physical activity, sport participation, and perceived competence in children. Our aim was to examine the drop-landing strategies of young children focusing on the lower extremity with a multi-variant approach. Forty-four children divided into four age groups (G1:3-4.5 y, G2:4.5-6 y, G3:6-7.5 y, G4:7.5-9 y) performed 20 drop-land trials in four different conditions: predictable stationary landing, running to the left, to the right, and stay in place. Fifteen reflective markers, two force plates, and ten surface electromyography (sEMG) sensors were used to collect data. MANOVAs (Group x Condition) were conducted separately for the kinematic, kinetic, and sEMG variables. Only significant group effects were found (kinematic MANOVA p = 0.039, kinetic MANOVA p = 0.007, and sEMG MANOVA p = 0.012), suggesting that younger groups (G1, G2) differed to the older groups (G3, G4). G1 showed less knee flexion and slower ankle dorsi-flexion during the braking phase compared to G3, while G2 presented smaller ankle dorsi-flexion at the braking phase and smaller ankle range of motion than G3. Overall kinetic variables analysis showed a group difference but no group differences for any single kinetic variable alone was found. Regarding sEMG, G1 during the flight phase exhibited longer tibialis anterior and hamstrings activity than G3 and G3 & G4, respectively; and an earlier start of the hamstrings' impact burst than G4. In addition, distal to proximal control was primarily used by all groups to coordinate muscle activity (in response to impact) and joint motion (after impact). Perhaps a developmental critical point in landing performance exists at 4-5 years of age since G1 presented the largest differences among the groups. This suggests that to improve landing strategies could start around this age. Future studies should examine if playground environments that include equipment conducive to landing and practitioners in the kindergarten schools are adequate vehicles to empower this type of intervention.

A literature overview of modern biomechanical-based technologies for bike-fitting professionals and coaches

The optimization of cycling position is essential to improve performance and prevent overuse injuries. Bike-fitting methods, based on biomechanical variables, have been proposed in the scientific literature. To facilitate and generalize their use, the bike-fitting industry has developed various technologies to study and analyze the cycling position. The vast majority of bike-fitting protocols are based on joint kinematics, which can be evaluated in laboratory with two- or three-dimensional motion analysis systems. Joint kinematics can also be assessed in outdoor conditions with inertial measurement units, but currently, these tools provide a limited number of variables compared to laboratory systems. In addition, the bike-fitting professional can analyse pedalling technique with pedal forces to understand the effects of the bike adjustments on pedalling effectiveness. To complete the biomechanical evaluation, pressure mapping sensors allow for the measurement of the pressure load and distribution on the interfaces between the cyclist and the bicycle to detect imbalances and choose bike components (e.g., saddle). To go further in the analysis, muscular activity can be assessed with surface electromyography sensors to detect imbalances or asymmetry. The aim of this literature overview is to clearly define the role of these technologies in a bike-fitting protocol and identify their characteristics and limitations while proposing perspectives for future developments. Therefore, this work is intended for bike-fitting professionals and coaches wishing to choose the most suitable technologies to study and improve the cycling position, and for the bike-fitting industry, in order to optimize existing technologies and help develop new concepts.

2.5D Laser-Cutting-Based Customized Fabrication of Long-Term Wearable Textile sEMG Sensor: From Design to Intention Recognition

The surface electromyography(sEMG) sensor is widely used as a human-machine interface in wearable systems. Although numerous studies have applied compact sEMG systems to wearable devices, these are inconvenient and not suitable for long-term use. Herein, we introduce a 2.5D laser cutting method to accelerate customized sensor fabrication from design to production. The customized textile-based sensor provides high wearing comfort and improves the sensor signal quality through stable contact. We implemented a foam-filled electrode to ensure solid skin-electrode contact even during perspiration and varying pressure conditions, and evaluated its performance experimentally. The sensor-integrated garments one for the leg and one for the arm were fabricated with the proposed design method for further evaluation and application. Consistent sensor performance was demonstrated during squats and running (i.e., perspiration) while wearing the leg sensor. The sensor sleeve for the arm was integrated with intention recognition algorithms for hand gesture recognition. A Convolutional Neural Network (CNN) architecture was employed to classify 28 hand gestures, including finger and wrist motions. The average classification accuracy of five subjects achieved 93.21%, and further increased to 94.34% after perspiration.

Fused ultrasound and electromyography-driven neuromuscular model to improve plantarflexion moment prediction across walking speeds

BackgroundImproving the prediction ability of a human-machine interface (HMI) is critical to accomplish a bio-inspired or model-based control strategy for rehabilitation interventions, which are of increased interest to assist limb function post neurological injuries. A fundamental role of the HMI is to accurately predict human intent by mapping signals from a mechanical sensor or surface electromyography (sEMG) sensor. These sensors are limited to measuring the resulting limb force or movement or the neural signal evoking the force. As the intermediate mapping in the HMI also depends on muscle contractility, a motivation exists to include architectural features of the muscle as surrogates of dynamic muscle movement, thus further improving the HMI’s prediction accuracy.ObjectiveThe purpose of this study is to investigate a non-invasive sEMG and ultrasound (US) imaging-driven Hill-type neuromuscular model (HNM) for net ankle joint plantarflexion moment prediction. We hypothesize that the fusion of signals from sEMG and US imaging results in a more accurate net plantarflexion moment prediction than sole sEMG or US imaging.MethodsTen young non-disabled participants walked on a treadmill at speeds of 0.50, 0.75, 1.00, 1.25, and 1.50 m/s. The proposed HNM consists of two muscle-tendon units. The muscle activation for each unit was calculated as a weighted summation of the normalized sEMG signal and normalized muscle thickness signal from US imaging. The HNM calibration was performed under both single-speed mode and inter-speed mode, and then the calibrated HNM was validated across all walking speeds.ResultsOn average, the normalized moment prediction root mean square error was reduced by 14.58 % (p=0.012) and 36.79 % (p<0.001) with the proposed HNM when compared to sEMG-driven and US imaging-driven HNMs, respectively. Also, the calibrated models with data from the inter-speed mode were more robust than those from single-speed modes for the moment prediction.ConclusionsThe proposed sEMG-US imaging-driven HNM can significantly improve the net plantarflexion moment prediction accuracy across multiple walking speeds. The findings imply that the proposed HNM can be potentially used in bio-inspired control strategies for rehabilitative devices due to its superior prediction.

American Sign Language Translation Using Wearable Inertial and Electromyography Sensors for Tracking Hand Movements and Facial Expressions.

A sign language translation system can break the communication barrier between hearing-impaired people and others. In this paper, a novel American sign language (ASL) translation method based on wearable sensors was proposed. We leveraged inertial sensors to capture signs and surface electromyography (EMG) sensors to detect facial expressions. We applied a convolutional neural network (CNN) to extract features from input signals. Then, long short-term memory (LSTM) and transformer models were exploited to achieve end-to-end translation from input signals to text sentences. We evaluated two models on 40 ASL sentences strictly following the rules of grammar. Word error rate (WER) and sentence error rate (SER) are utilized as the evaluation standard. The LSTM model can translate sentences in the testing dataset with a 7.74% WER and 9.17% SER. The transformer model performs much better by achieving a 4.22% WER and 4.72% SER. The encouraging results indicate that both models are suitable for sign language translation with high accuracy. With complete motion capture sensors and facial expression recognition methods, the sign language translation system has the potential to recognize more sentences.

Human lower limb activity recognition techniques, databases, challenges and its applications using sEMG signal: an overview.

Human lower limb activity recognition (HLLAR) has grown in popularity over the last decade mainly because to its applications in the identification and control of neuromuscular disorders, security, robotics, and prosthetics. Surface electromyography (sEMG) sensors provide various advantages over other wearable or visual sensors for HLLAR applications, including quick response, pervasiveness, no medical monitoring, and negligible infection. Recognizing lower limb activity from sEMG signals is also challenging owing to the noise in the sEMG signal. Pre- processing of sEMG signals is extremely desirable before the classification because they allow a more consistent and precise evaluation in the above applications. This article provides a segment-by-segment overview of: (1) Techniques for eliminating artifacts from sEMG signals from the lower limb. (2) A survey of existing datasets of lower limb sEMG. (3) A concise description of the various techniques for processing and classifying sEMG data for various applications involving lower limb activity. Finally, an open discussion is presented, which may result in the identification of a variety of future research possibilities for human lower limb activity recognition. Therefore, it is possible to anticipate that the framework presented in this study can aid in the advancement of sEMG-based recognition of human lower limb activity.

Development of a Non-Contacting Muscular Activity Measurement System for Evaluating Knee Extensors Training in Real-Time.

To give people more specific information on the quality of their daily motion, it is necessary to continuously measure muscular activity during everyday occupations in an easy way. The traditional methods to measure muscle activity using a combination of surface electromyography (sEMG) sensors and optical motion capture system are expensive and not suitable for non-technical users and unstructured environment. For this reason, in our group we are researching methods to estimate leg muscle activity using non-contact wearable sensors, improving ease of movement and system usability. In a previous study, we developed a method to estimate muscle activity via only a single inertial measurement unit (IMU) on the shank. In this study, we describe a method to estimate muscle activity during walking via two IMU sensors, using an original sensing system and specifically developed estimation algorithms based on ANN techniques. The muscle activity estimation results, estimated by the proposed algorithm after optimization, showed a relatively high estimation accuracy with a correlation efficient of R2 = 0.48 and a standard deviation STD = 0.10, with a total system average delay of 192 ms. As the average interval between different gait phases in human gait is 250–1000 ms, a 192 ms delay is still acceptable for daily walking requirements. For this reason, compared with the previous study, the newly proposed system presents a higher accuracy and is better suitable for real-time leg muscle activity estimation during walking.

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

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.

Evaluating the impact of writing surface and configuration on muscle activation level during a handwriting task: An exploratory study.

Tablets are ubiquitous in workplaces and schools. However, there have been limited studies investigating the effect tablets have on the body during digital writing activities. This study investigated the biomechanical impact of writing interface design (paper, whiteboard, and tablet) and orientation (horizontal, 45°, and vertical) on tablet users. Fourteen adults (7 male, 7 female) participated in a study during which they performed simple writing tasks. Surface electromyography (sEMG) sensors were used to measure upper extremity muscle activation. Results indicate that the effects of writing surface type were most pronounced in forearm muscle activation. Specifically, in the extensor carpi radialis (ECR), where muscle activity was lower on the tablet PC surface. The effects of writing configuration were prominent in the shoulder and forearm. The activation of the flexor carpi ulnaris (FCU) and trapezius muscles was significantly lower in the 45° configuration. An exception to the efficacy of this configuration was the anterior deltoid muscle, which exhibited the lowest muscle activity in the horizontal orientation. Tablet surface and the 45° configuration resulted in the lowest muscle activation levels. Future studies should include longer experiment duration to investigate the effects of continuous writing.

Non-Invasive Muscular Atrophy Causes Evaluation for Limb Fracture Based on Flexible Surface Electromyography System.

Muscular atrophy after limb fracture is a frequently occurring complication with multiple causes. Different treatments and targeted rehabilitation procedures should be carried out based on the causes. However, bedside evaluation methods are invasive in clinical practice nowadays, lacking reliable non-invasive methods. In this study, we propose a non-invasive flexible surface electromyography system with machine learning algorithms to distinguish nerve-injury and limb immobilization-related atrophy. First, a flexible surface electromyography sensor was designed and verified by in vitro tests for its robustness and flexibility. Then, in vivo tests on rats proved the reliability compared with the traditional invasive diagnosis method. Finally, this system was applied for the diagnosis of muscular atrophy in 10 patients. The flexible surface electromyography sensor can achieve a max strain of 12.0%, which ensures close contact with the skin. The in vivo tests on rats show great comparability with the traditional invasive diagnosis method. It can achieve a high specificity of 95.28% and sensitivity of 98.98%. Application on patients reaches a relatively high specificity of 89.44% and sensitivity of 91.94%. The proposed painless surface electromyography system can be an easy and accurate supplementary for bedside muscular atrophy causes evaluation, holding excellent contact with the body.

Development of an Integrated Virtual Reality System with Wearable Sensors for Ergonomic Evaluation of Human-Robot Cooperative Workplaces.

This work proposes a novel virtual reality system which makes use of wearable sensors for testing and validation of cooperative workplaces from the ergonomic point of view. The main objective is to show, in real time, the ergonomic evaluation based on a muscular activity analysis within the immersive virtual environment. The system comprises the following key elements: a robotic simulator for modeling the robot and the working environment; virtual reality devices for human immersion and interaction within the simulated environment; five surface electromyographic sensors; and one uniaxial accelerometer for measuring the human ergonomic status. The methodology comprises the following steps: firstly, the virtual environment is constructed with an associated immersive tutorial for the worker; secondly, an ergonomic toolbox is developed for muscular analysis. This analysis involves multiple ergonomic outputs: root mean square for each muscle, a global electromyographic score, and a synthetic index. They are all visualized in the immersive environment during the execution of the task. To test this methodology, experimental trials are conducted on a real use case in a human–robot cooperative workplace typical of the automotive industry. The results showed that the methodology can effectively be applied in the analysis of human–robot interaction, to endow the workers with self–awareness with respect to their physical conditions.

A parallel classification strategy to simultaneous control elbow, wrist, and hand movements

BackgroundIn the field of myoelectric control systems, pattern recognition (PR) algorithms have become always more interesting for predicting complex electromyography patterns involving movements with more than 2 Degrees of Freedom (DoFs). The majority of classification strategies, used for the prosthetic control, are based on single, hierarchical and parallel linear discriminant analysis (LDA) classifiers able to discriminate up to 19 wrist/hand gestures (in the 3-DoFs case), considering both combined and discrete motions. However, these strategies were introduced to simultaneously classify only 2 DoFs and their use is limited by the lack of online performance measures. This study introduces a novel classification strategy based on the Logistic Regression (LR) algorithm with regularization parameter to provide simultaneous classification of 3 DoFs motion classes.MethodsThe parallel PR-based strategy was tested on 15 healthy subjects, by using only six surface EMG sensors. Twenty-seven discrete and complex elbow, hand and wrist motions were classified by keeping the number of electromyographic (EMG) electrodes to a bare minimum and the classification error rate under 10 %. To this purpose, the parallel classification strategy was implemented by using three classifiers one for each DoF: the “Elbow classifier”, the “Wrist classifier”, and the “Hand classifier” provided the simultaneous control of the elbow, hand, and wrist joints, respectively.ResultsBoth the offline and real-time performance metrics were evaluated and compared with the LDA parallel classification results. The real-time recognition results were statistically better with the LR classifier with respect to the LDA classifier, for all motion classes (elbow, hand and wrist).ConclusionsIn this paper, a novel parallel PR-based strategy was proposed for classifying up to 3 DoFs: three joint classifiers were employed simultaneously for classifying 27 motion classes related to the elbow, wrist, and hand and promising results were obtained.

Detection of swallowing disorders with a multiple-channel surface electromyography sensor sheet

Background/purposeA sensor sheet comprising multiple electromyography electrodes that can be used to measure a series of multiple muscle activities related to swallowing was recently developed. In this study, we aimed to evaluate its utility in detecting swallowing disorders with a new method for the identification of muscle activity. Materials and methodsAll participants were evaluated by using the repetitive saliva swallowing test and modified water swallowing test and were classified accordingly into participants with (n = 21; mean age, 81.6 [standard deviation, 10.1] years) and those without (n = 41; mean age, 70.0 [8.4] years) dysphagia. The sheet contains four pairs of electrodes, and surface electromyography (sEMG) was performed on the suprahyoid (position A: upper front of the suprahyoid muscles; position B: bottom rear of the suprahyoid muscles) and infrahyoid (position C: above the infrahyoid muscles; position D: beneath the infrahyoid muscles) muscles while the participants swallowed liquid, thickened water, paste, and jelly. The sEMG findings, including the duration of swallowing waveforms and the delay in swallowing onset from position A to positions B, C, and D, were compared between the groups. ResultsThe duration of muscle activity differed between the groups in the infrahyoid muscles when jelly (Mann–Whitney U test; position C, P = 0.007 and position D, P = 0.018) and thickened water (position C, P = 0.033) were swallowed. ConclusionOur study demonstrates the utility of a novel sensor sheet developed for detecting swallowing disorders by using visual methods for identification of muscle activity.

Neuromechanical Responses to Spinal Manipulation and Mobilization: A Crossover Randomized Clinical Trial

ObjectiveThe purpose of this study was to compare the immediate effect of spinal manipulation (SMa) and spinal mobilization (SMo) on muscular responses, spinal stiffness, and segmental spinal pressure evoked pain in a population of participants with chronic middle back pain (MBP). MethodsIn a crossover randomized trial, 2 experienced chiropractors assessed whether volunteers were eligible for the protocol according to a list of specific inclusion and exclusion criteria. Individuals with MBP participated in 2 experimental sessions within 72 hours. During the first session, participants randomly received a SMa or SMo delivered by an apparatus using a servolinear motor. During the second session, the other modality was delivered. Spinal stiffness and pressure-provoked pain intensity outcomes were assessed before and after each therapy, and muscular responses were recorded during the treatment using surface electromyographic sensors. Signed-rank Wilcoxon tests for muscular responses and generalized model for repeated measure for spinal stiffness and pressure-provoked pain were used for statistical analyses. ResultsAmong the 32 potential participants, 26 (mean age 29.9 [±9.14], 15 women) completed both sessions. Between-group differences were observed for the muscular response amplitude (P < .001), and indeed the normalized RMS muscular response was found to be higher during SMa than SMo. Similar results were observed for pressure-provoked pain intensity at the level of therapeutic modality application (P = .002) as a higher decrease in pain was found after SMa (47.9 [±22.8] to 36.6 [±23.7]) compared with SMo (47.2 [±23.2] to 45.5 [±24.3]). No between-group differences were found for spinal stiffness change, nor for terminal (P = .08) and global spinal stiffness (P = .06). ConclusionIn a controlled environment, spinal manipulation and mobilization generated different muscle responses and had different immediate effects on pressure-provoked pain intensity for participants with MBP.

Finger Gesture Recognition Using Sensing and Classification of Surface Electromyography Signals With High-Precision Wireless Surface Electromyography Sensors.

Finger gesture recognition (FGR) plays a crucial role in achieving, for example, artificial limb control and human-computer interaction. Currently, the most common methods of FGR are visual-based, voice-based, and surface electromyography (EMG)-based ones. Among them, surface EMG-based FGR is very popular and successful because surface EMG is a cumulative bioelectric signal from the surface of the skin that can accurately and intuitively represent the force of the fingers. However, existing surface EMG-based methods still cannot fully satisfy the required recognition accuracy for artificial limb control as the lack of high-precision sensor and high-accurate recognition model. To address this issue, this study proposes a novel FGR model that consists of sensing and classification of surface EMG signals (SC-FGR). In the proposed SC-FGR model, wireless sensors with high-precision surface EMG are first developed for acquiring multichannel surface EMG signals from the forearm. Its resolution is 16 Bits, the sampling rate is 2 kHz, the common-mode rejection ratio (CMRR) is less than 70 dB, and the short-circuit noise (SCN) is less than 1.5 μV. In addition, a convolution neural network (CNN)-based classification algorithm is proposed to achieve FGR based on acquired surface EMG signals. The CNN is trained on a spectrum map transformed from the time-domain surface EMG by continuous wavelet transform (CWT). To evaluate the proposed SC-FGR model, we compared it with seven state-of-the-art models. The experimental results demonstrate that SC-FGR achieves 97.5% recognition accuracy on eight kinds of finger gestures with five subjects, which is much higher than that of comparable models.

Usability Assessment of Body Controlled Electric Hand Prostheses: A Pilot Study.

Poly-articulated hands, actuated by multiple motors and controlled by surface myoelectric technologies, represent the most advanced aids among commercial prostheses. However, simple hook-like body-powered solutions are still preferred for their robustness and control reliability, especially for challenging environments (such as those encountered in manual work or developing countries). This study presents the mechatronic implementation and the usability assessment of the SoftHand Pro-Hybrid, a family of poly-articulated, electrically-actuated, and body-controlled artificial hands, which combines the main advantages of both body-powered and myoelectric systems in a single device. An assessment of the proposed system is performed with individuals with and without limb loss, using as a benchmark the SoftHand Pro, which shares the same soft mechanical architecture, but is controlled using surface electromyographic sensors. Results indicate comparable task performance between the two control methods and suggest the potential of the SoftHand Pro-Hybrid configurations as a viable alternative to myoelectric control, especially in work and demanding environments.