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Featured Application: Most existing research centers on examining the effects of sit–stand desks on sitting and standing positions, often overlooking the influence of the workplace environment and seat tilt angles on comfort. This study develops and constructs a model of a standing desk chair to address this gap and explores how seat tilt angles and various task conditions impact chair comfort. In this article, researchers adopt an innovative approach to evaluate the comfort of standing desk chairs, integrating the analysis of pressure distribution and electromyography signals. This holistic method not only delivers an objective assessment of chair comfort but also sheds light on how chairs affect muscle activity. Through the analysis of pressure distribution, researchers gain insight into how standing desk chairs support different body parts, enabling evaluation of their overall comfort and supportiveness. Furthermore, by incorporating electromyography signal analysis, researchers delve into the impact of chairs on muscle activity, thereby facilitating a more comprehensive evaluation of chair comfort. The distinctiveness of this integrated analytical approach lies in its consideration of both pressure distribution on the seated body and changes in muscle activity, resulting in a thorough and precise assessment of chair comfort. This research methodology provides valuable insights and guidance for the design and enhancement of standing desk chairs, ultimately improving user comfort and satisfaction. This study explores the health consequences of prolonged sitting by introducing a standing chair and examining the effects of seat tilt angles on comfort. Using synchronized pressure distribution testing and surface electromyographic (sEMG) signal analysis, we assessed pressure and sEMG responses at tilt angles of 0°, 20°, and 40° during tasks such as computer work, writing, and ironing. Initial measurements with body pressure distribution sensors targeted the buttocks and feet, while surface electromyographic equipment captured signals from the bilateral lumbar erector spinae, vastus lateralis, and gastrocnemius muscles. MATLAB processing facilitated the analysis of integrated electromyographic values and mean power frequencies, elucidating the effects of tilt angles on comfort. Our research findings indicate that a 20° tilt angle significantly enhances comfort during computer and writing tasks and noticeably increases the comfort of the erector spinae muscles during ironing. Conversely, a 0° tilt angle is more beneficial for the vastus lateralis and gastrocnemius muscles. These results underscore the importance of selecting appropriate tilt angles to improve comfort across various tasks. Furthermore, integrating pressure sensors and surface electromyographic signals enables a comprehensive evaluation of chair ergonomic quality, offering valuable insights for chair design optimization. [ABSTRACT FROM AUTHOR]

The integration of artificial intelligence (AI) models in the classification of electromyographic (EMG) signals represents a significant advancement in the design of control systems for prostheses. This study explores the development of a portable system that classifies the electrical activity of three shoulder muscles in real time for actuator control, marking a milestone in the autonomy of prosthetic devices. Utilizing low-power microcontrollers, the system ensures continuous EMG signal recording, enhancing user mobility. Focusing on a case study—a 42-year-old man with left shoulder disarticulation—EMG activity was recorded over two days using a specifically designed electronic board. Data processing was performed using the Edge Impulse platform, renowned for its effectiveness in implementing AI on edge devices. The first day was dedicated to a training session with 150 repetitions spread across 30 trials and three different movements. Based on these data, the second day tested the AI model's ability to classify EMG signals in new movement executions in real time. The results demonstrate the potential of portable AI-based systems for prosthetic control, offering accurate and swift EMG signal classification that enhances prosthetic user functionality and experience. This study not only underscores the feasibility of real-time EMG signal classification but also paves the way for future research on practical applications and improvements in the quality of life for prosthetic users. [ABSTRACT FROM AUTHOR]

This work presents a semi-supervised multilayer neural network (MLNN) with an Autoencoder to develop a classification model for recognizing hand gestures from electromyographic (EMG) signals. Using a Myo armband equipped with eight non-invasive surface-mounted biosensors, raw surface EMG (sEMG) sensor data were captured corresponding to five hand gestures: Fist, Open hand, Wave in, Wave out, and Double tap. The sensor collected data underwent preprocessing, feature extraction, label assignment, and dataset organization for classification tasks. The model implementation, validation, and testing demonstrated its efficacy after incorporating synthetic sEMG data generated by an Autoencoder. In comparison to the state-of-the-art techniques from the literature, the proposed model exhibited strong performance, achieving accuracy of 99.68%, 100%, and 99.26% during training, validation, and testing, respectively. Comparatively, the proposed MLNN with Autoencoder model outperformed a K-Nearest Neighbors model established for comparative evaluation.

The lower limb exoskeleton technology is designed to facilitate the movement of human lower limbs. Significant progress has been made in this technology, which has important implications for rehabilitation patients and individuals who are eager to enhance their mobility. Electromyogram (EMG) signals, which encompass the complexity of human physiology, are integrated into lower limb exoskeletons due to their deep connection to movement and predictability before movement begins, and this integration is expected to enable intelligent control and improved human-computer interaction. This review explores a pattern based on EMG signals for identifying human motor intent in lower limb exoskeletons. Firstly, the development of lower limb exoskeleton and the existing lower limb exoskeleton products are systematically described. Combined with the intelligent control system of wearable device, the main methods and research progress of recognizing the motion intention of lower limb exoskeleton by surface EMG are discussed. It shows that the use of surface EMG can effectively improve the human-machine interaction of lower limb exoskeleton. Together, the study provides insight into the challenges that are hindering the commercialization of the market and provides a perspective on the future development of EMG signals.

In the present work, decision trees are employed to determine the activation patterns in electromyographic signals in multiple muscles of interest. Due to the interaction of several muscles when performing a movement, it is common for several muscles to activate simultaneously. The problem is to determine if any muscle of interest is active and, in the case of multiple active muscles, which of them is the predominant one. In turn, we are interested in determining if a muscle is active enough to associate it with a voluntary movement. The proposed scheme is based on decision trees (which are supervised learning strategies for classification), and the envelope of a signal as a classification feature. The detection of an activation is performed in two stages: the first stage determines the candidate muscle to be active and a second stage determines its activation level. The algorithm was tested in control of a robotic arm in real time. The activation of a muscle is associated with a control command of a DC motor of the robotic arm. The proposed algorithm made it possible to manipulate the movements of that robotic arm to perform a simple task using electromyographic signals. [ABSTRACT FROM AUTHOR]

Aims: To investigate the effects of one‐time soft tissue therapy (STT) on pelvic floor muscle (PFM) electromyographic signals in women with stress and/or mixed urinary incontinence. Methods: An intervention study conducted with 63 women with stress and/or mixed urinary incontinence. Participants were randomly assigned to either the one‐time STT group (experimental group) or the control group. The same teaching model for voluntary contraction and relaxation of the PFM was used for all participants. Electromyographic signals from the PFM during functional tasks were the primary clinical outcome measures at baseline and immediately after the intervention. Electromyographic signals were analyzed using root mean square amplitude. Results: There was no significant difference between groups in electromyographic PFM signals in prebaseline rest (mean difference: −0.146 [95% confidence interval (CI): −0.44 to 0.148; p = 0.470]), phasic contractions (mean difference: 0.807 [95% CI: 0.123–1.491; p = 0.459]), tonic contractions (mean difference: 1.06 [95% CI: 0.255–1.865; p = 0.302]), endurance contractions (mean difference: 0.896 [95% CI: 0.057–1.735; p = 0.352]) and postbaseline rest (mean difference: −0.123 [95% CI: −0.406 to 0.16; p = 0.591]) immediately after the one‐time STT intervention. Conclusion: A one‐time STT intervention does not appear to effectively alter electromyographic signal of the PFM in women with urinary incontinence. Due to the limitations of the study, further research is needed to confirm these results. [ABSTRACT FROM AUTHOR]

This study explores the health consequences of prolonged sitting by introducing a standing chair and examining the effects of seat tilt angles on comfort. Using synchronized pressure distribution testing and surface electromyographic (sEMG) signal analysis, we assessed pressure and sEMG responses at tilt angles of 0°, 20°, and 40° during tasks such as computer work, writing, and ironing. Initial measurements with body pressure distribution sensors targeted the buttocks and feet, while surface electromyographic equipment captured signals from the bilateral lumbar erector spinae, vastus lateralis, and gastrocnemius muscles. MATLAB processing facilitated the analysis of integrated electromyographic values and mean power frequencies, elucidating the effects of tilt angles on comfort. Our research findings indicate that a 20° tilt angle significantly enhances comfort during computer and writing tasks and noticeably increases the comfort of the erector spinae muscles during ironing. Conversely, a 0° tilt angle is more beneficial for the vastus lateralis and gastrocnemius muscles. These results underscore the importance of selecting appropriate tilt angles to improve comfort across various tasks. Furthermore, integrating pressure sensors and surface electromyographic signals enables a comprehensive evaluation of chair ergonomic quality, offering valuable insights for chair design optimization.

The integration of artificial intelligence (AI) models in the classification of electromyographic (EMG) signals represents a significant advancement in the design of control systems for prostheses. This study explores the development of a portable system that classifies the electrical activity of three shoulder muscles in real time for actuator control, marking a milestone in the autonomy of prosthetic devices. Utilizing low-power microcontrollers, the system ensures continuous EMG signal recording, enhancing user mobility. Focusing on a case study—a 42-year-old man with left shoulder disarticulation—EMG activity was recorded over two days using a specifically designed electronic board. Data processing was performed using the Edge Impulse platform, renowned for its effectiveness in implementing AI on edge devices. The first day was dedicated to a training session with 150 repetitions spread across 30 trials and three different movements. Based on these data, the second day tested the AI model’s ability to classify EMG signals in new movement executions in real time. The results demonstrate the potential of portable AI-based systems for prosthetic control, offering accurate and swift EMG signal classification that enhances prosthetic user functionality and experience. This study not only underscores the feasibility of real-time EMG signal classification but also paves the way for future research on practical applications and improvements in the quality of life for prosthetic users.

Hand gesture recognition has been applied to many research fields and has shown its prominent advantages in increasing the practicality of Human-Robot Interaction (HRI). The development of advanced techniques in data science, such as big data and machine learning, facilitate the accurate classification of the hand gestures using electromyography (EMG) signals. However, the processing of the collection and label of the large data set imposes a high work burden and results in time-consuming implementations. Therefore, a novel method is proposed to combine the benefits of depth vision learning and EMG based hand gesture recognition. It is capable of labeling the class of the collected EMG data under the guidance of depth vision automatically, without consideration of the hand motion sequence. Finally, we demonstrated the proposed method for recognizing the ten hand gestures using a Myo armband. The experiment is set in a supervised learning way to evaluate the performance of the designed Hk-means algorithm. It shows that the proposed method can succeed in hand gesture recognition without labeling the data in advance. [ABSTRACT FROM AUTHOR]

Human-assisting intelligent systems demand certain methods to precisely predict motorized limb joint angles. This paper presents the application of deep-recurrent neural networks (RNNs), which is a type of neural network for processing sequential data, for predicting the knee joint angle in real-time. This model is created based on a combination of electromyographic (EMG) signals, (with electrodes being placed on three leg muscles), and inertial measurements of the upper and lower legs. The data collected from different subjects when they performed different gaits were used to construct the model, which was evaluated in a real-time setting. The proposed RNN model based on fusion information contains a balance between computational complexity and prediction accuracy. Results on a microcontroller show that, within a predicted horizon of 50 ms, the model has a low prediction error of ±2.93 degrees. [ABSTRACT FROM AUTHOR]

In the current study, our research group proposed an asymmetric lower extremity exoskeleton to enable above-knee amputees to walk with a load. Due to the absence of shank and foot, the knee and ankle joint at the amputation side of the exoskeleton lack tracking targets, so it is difficult to realize the function of assisted walking when going up and downstairs. Currently, the use of lower-limb electromyography to predict the angles of lower limb joints has achieved remarkable results. However, the prediction effect was poor when only using electromyography from the thigh. Therefore, this paper introduces hip-angle and plantar pressure signals for improving prediction effect and puts forward a joint prediction method of knee- and ankle-joint angles by electromyography of the thigh, hip-joint angle, and plantar pressure signals. The generalized regression neural network optimized by the golden section method is used to predict the joint angles. Finally, the parameters (the maximum error, the Root-Mean-Square error (RMSE), and correlation coefficient (γ)) were calculated to verify the feasibility of the prediction method.

Human grasping recognition based on superficial electromyographic signals is a research field that has grown in recent years due to the fact that it is possible to control i.) prosthesis hand, and ii.) general device. Recently, there is an innovative technology called wearable device; an example is MyoarmbandTM, which is able to capture superficial electromyographic signals. Using this device, a new mathematical model that allows automatic grasping movements detection, in real-time recognition, has been introduced. The model involves three steps: i.) identifying movement inception, ii.) extracting features from superficial electromyographic signals, and iii.) recognizing patterns. In order to evaluate hand grasping recognition accuracy, an experimental procedure is presented, using Myoarmband™ bracelet. Moreover, a potential application of the presented model focuses on the control of the ProMain soft robotic hand prosthesis. [ABSTRACT FROM AUTHOR]

Purpose: The state of athletes' muscles is not constant, but it differs depending on the stage of sports training, which is associated with different degrees of muscle fatigue. There is thus a need to find a non-invasive and simple method to assess muscle fatigue. The aim of the study was to determine the relationship between muscle fatigue due to physical effort and changes in skin temperature, measured using a thermographic camera. Methods: The study involved 12 volleyball players. The participants were to maintain 70% of peak torque in the joint for as long as possible. We measured peak torque and the time of maintaining 70% of its value (tlim) as well as continuously recording skin temperature and electromyographic (EMG) signals in the region of the belly of the rectus femoris. The measurements were taken twice: before and after a series of squats. Results: The study found that tlim decreased when isometric contraction was performed after physical effort. Pre- and post-exercise skin temperature did not differ significantly, however, the increase rates of temperature and the root mean square (RMS) of the EMG signals grew significantly. In most of the players, skin temperature also correlated with the RMS, median frequency (MDF), and mean frequency (MF) of the EMG signals. Conclusions: Measuring the time of maintaining submaximal torque during isometric contraction and the slope coefficient for the increase in temperature recorded using a thermographic camera can be a simple, cost-effective, and non-invasive method of assessing fatigue and efficiency decreases in the muscles in volleyball players. [ABSTRACT FROM AUTHOR]

In this paper, we provide a robust framework to detect anomalous electromyographic (EMG) signals and identify contamination types. As a first step for feature selection, optimally selected Lawton wavelets transform is applied. Robust principal component analysis (rPCA) is then performed on these wavelet coefficients to obtain features in a lower dimension. The rPCA based features are used for constructing a self-organizing map (SOM). Finally, hierarchical clustering is applied on the SOM that separates anomalous signals residing in the smaller clusters and breaks them into logical units for contamination identification. The proposed methodology is tested using synthetic and real world EMG signals. The synthetic EMG signals are generated using a heteroscedastic process mimicking desired experimental setups. A sub-part of these synthetic signals is introduced with anomalies. These results are followed with real EMG signals introduced with synthetic anomalies. Finally, a heterogeneous real world data set is used with known quality issues under an unsupervised setting. The framework provides recall of 90% (± 3.3) and precision of 99%(±0.4). [ABSTRACT FROM PUBLISHER]

The purposes of this study were to verify and compare the performances of anaerobic threshold (AT) point estimates among different filtering intervals (9, 15, 20, 25, 30 s) and to investigate the interrelationships of AT point estimates obtained by ventilatory threshold (VT) and muscle fatigue thresholds using electromyographic (EMG) activity during incremental exercise on a cycle ergometer. 69 untrained male university students, yet pursuing regular exercise voluntarily participated in this study. The incremental exercise protocol was applied with a consistent stepwise increase in power output of 20 watts per minute until exhaustion. AT point was also estimated in the same manner using V-slope program with gas exchange parameters. In general, the estimated values of AT point-time computed by EMG method were more consistent across 5 filtering intervals and demonstrated higher correlations among themselves when compared with those values obtained by VT method. The results found in the present study suggest that the EMG signals could be used as an alternative or a new option in estimating AT point. Also the proposed computing procedure implemented in Matlab for the analysis of EMG signals appeared to be valid and reliable as it produced nearly identical values and high correlations with VT estimates. [ABSTRACT FROM AUTHOR]

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Intermuscular coherence can identify oscillatory coupling between two electromyographic (EMG) signals, measuring common presynaptic drive to motor neurons. Beta band oscillations (15-30 Hz) are hypothesized to originate largely from primary motor cortex, and are reduced during dynamic relative to static motor tasks. It has yet to be established whether motor imagery modulates beta intermuscular coherence. Using visual feedback, 10 unimpaired participants completed eighteen trials of pinching their right thumb and index finger at a constant force. During the 60-second trials, participants simultaneously engaged in one of three types of kinesthetic imagery: the right thumb and index finger executing a constant force pinch (static), the fingers of the right hand sequentially flexing and extending (dynamic), and the right foot pushing down with constant force (foot). Motor imagery of a dynamic motor task resulted in significantly lower intermuscular beta coherence than imagery of a static motor pinch task, without any difference in task performance or root-mean-square EMG. Thus, motor imagery affects intermuscular coherence in the beta band, even while measures of task performance remain constant. This finding provides insight for incorporation of beta band intermuscular coherence in future motor rehabilitation schemes and brain computer interface design. [ABSTRACT FROM AUTHOR]

Background:Paralysis of abdominal muscles is the main cause of respiratory dysfunctions in patients with lower cervical spinal cord lesion. Activation of the abdominal muscles using functional electrical stimulation (FES) improved respiratory function in these patients. But application of FES frequently requires a caregiver, and it may not be well synchronized with the patient's respiratory activity.Objective:To perform preliminary examination of electromyographic (EMG)-activated FES for caregiver-independent and synchronized cough and expiration induction in tetraplegia.Design:Self-controlled study.Setting:Loewenstein Rehabilitation Center, Raanana, Israel.Subjects:A total of 10 male patients with complete or almost complete tetraplegia.Main outcome measures:Peak expiratory flow (PEF), forced vital capacity (FVC), and maximal voluntary ventilation (MVV).Methods: The outcome measures were examined with the abdominal muscles unassisted or assisted by various methods. These included manual assistance or application of FES, activated by a caregiver, by the patient, or by EMG signals elicited from the patient's muscle.Results:Manual assistance improved the mean PEF value by 36.7% (P<0.01) and the mean FVC value by 15.4% (P=0.01). FES did not significantly change most measurements, and patient-activated FES even reduced PEF (P<0.05). But following EMG-activated FES PEF and FVC values were higher than those following patient-activated FES (P<0.05 for PEF; P<0.01 for FVC), and their mean values were higher by 15.8 and 18.9%, respectively.Conclusions:Abdominal FES failed to improve respiratory function in this study, but applying FES to abdominal muscles by EMG from the patient's muscle may promote caregiver-free respiration and coughing in persons with cervical SCL.Spinal Cord (2007) 45, 491–495; doi:10.1038/sj.sc.3102039; published online 27 February 2007 [ABSTRACT FROM AUTHOR]

In the current study, our research group proposed an asymmetric lower extremity exoskeleton to enable above-knee amputees to walk with a load. Due to the absence of shank and foot, the knee and ankle joint at the amputation side of the exoskeleton lack tracking targets, so it is difficult to realize the function of assisted walking when going up and downstairs. Currently, the use of lower-limb electromyography to predict the angles of lower limb joints has achieved remarkable results. However, the prediction effect was poor when only using electromyography from the thigh. Therefore, this paper introduces hip-angle and plantar pressure signals for improving prediction effect and puts forward a joint prediction method of knee- and ankle-joint angles by electromyography of the thigh, hip-joint angle, and plantar pressure signals. The generalized regression neural network optimized by the golden section method is used to predict the joint angles. Finally, the parameters (the maximum error, the Root-Mean-Square error (RMSE), and correlation coefficient (γ)) were calculated to verify the feasibility of the prediction method. [ABSTRACT FROM AUTHOR]

This study presents an event driven automatic controller to regulate the movement of a mobile lower limb active orthosis (LLAO) triggered with the information obtained from electromyographic (EMG) signals, which are captured from the user's triceps and biceps muscles. The proposed controller has an output feedback realization including a velocity estimator algorithm based on a high order sliding mode observer. The output feedback controller implements a class of decentralized super-twisting algorithm. The controller must enforce the movement of the orthosis articulations following some defined reference trajectories. This strategy realizes a time-window dependent event driven controller for the active orthosis. The controller selects among four different routines to be executed by a patient. A differential neural network classifies the different patterns of muscle movements. This classifier succeeds in defining the correct EMG class in a 95% of the tested signals. This work senses the EMG signals from the biceps and triceps, considering a possible injury in the patient to be obtained from the quadriceps. Therefore, four upper limb routines are established to generate the corresponding classes and the four different main therapies for the LLAO. A fully instrumented and self-designed orthosis is constructed to evaluate the proposed controller including three rotational joints per leg and a mobile robot to execute translation movements. [ABSTRACT FROM AUTHOR]