Surface Electromyography Sensors Research Articles

How to use one surface electromyography sensor to recognize six hand movements for a mechanical hand in real time: a method based on Morse code.

Surface electromyography (sEMG) signal is a kind of physiological signal reflecting muscle activity and muscle force. At present, the existing methods of recognizing human motion intention need more than two sensors to recognize more than two kinds of movements, the sensor pasting positions are special, and the hardware conditions for execution are high. In this work, a real-time motion intention recognition method based on Morse code is proposed and applied to the mechanical hand. The short-time and long-term muscle contraction signals collected by a single sEMG sensor were extracted and encoded with the Morse code method, and then the developed mapping method from Morse code to six hand movements were used to recognize hand movements. The average recognition accuracy of hand movements was 94.8704 ± 2.3556%, the average adjusting time was 34.89s for all subjects, and the execution time of a single movement was 381ms. The corresponding experiment video can be found in the attachment to show the experiment. The method proposed in this work is a method with one sensor to recognize six movements, low hardware conditions, high recognition accuracy, and insensitive to the difference of sensor pasting position.

Evaluating Desk-Assisted Standing Techniques for Simulated Pregnant Conditions: An Experimental Study Using a Maternity-Simulation Jacket.

Lower back pain, a common issue among pregnant women, often complicates daily activities like standing up from a chair. Therefore, research into the standing motion of pregnant women is important, and many research studies have already been conducted. However, many of these studies were conducted in highly controlled environments, overlooking everyday scenarios such as using a desk for support when standing up, and their effects have not been adequately tested. To address this gap, we measured multimodal signals for a sit-to-stand (STS) movement with hand assistance and verified the changes using a t-test. To avoid imposing strain on pregnant women, we used 10 non-diseased young adults who wore jackets designed to simulate pregnancy conditions, thus allowing for more comprehensive and rigorous experimentation. We attached surface electromyography (sEMG) sensors to the erector spinae muscles of participants and measured changes in muscle activity, skeletal positioning, and center of pressure both before and after wearing a Maternity-Simulation Jacket. Our analysis showed that the jacket successfully mimicked key aspects of the movement patterns typical in pregnant women. These results highlight the possibility of developing practical strategies that more accurately mirror the real-life scenarios met by pregnant women, enriching the current research on their STS movement.

Multi-layer CNN-LSTM network with self-attention mechanism for robust estimation of nonlinear uncertain systems.

With the help of robot technology, intelligent rehabilitation of patients with lower limb motor dysfunction caused by stroke can be realized. A key factor constraining the clinical application of rehabilitation robots is how to realize pattern recognition of human movement intentions by using the surface electromyography (sEMG) sensors to ensure unhindered human-robot interaction. A multilayer CNN-LSTM prediction network incorporating the self-attention mechanism (SAM) is proposed, in this paper, which can extract and learn the periodic and trend characteristics of the sEMG signals, and realize the accurate autoregressive prediction of the human motion information. Firstly, the multilayer CNN-LSTM network utilizes the CNN layer for initial feature extraction of data, and the LSTM network is used to improve the enhancement of the historical time-series features. Then, the SAM is used to improve the global feature extraction performance and parallel computation speed of the network. In comparison with existing test is carried out using actual data from five healthy subjects as well as a clinical hemiplegic patient to verify the superiority and practicality of the proposed algorithm. The results show that most of the model's prediction R > 0.9 for different motion states of healthy subjects; in the experiments oriented to the motion characteristics of patient subjects, the angle prediction results of R > 0.99 for the untrained data on the affected side, which proves that our proposed model also has a better effect on the angle prediction of the affected side. The main contribution of this paper is to realize continuous motion estimation of ankle joint for healthy and hemiplegic individuals under non-ideal conditions (weak sEMG signals, muscle fatigue, high muscle tension, etc.), which improves the pattern recognition accuracy and robustness of the sEMG sensor-based system.

Estimation of lower back muscle force in a lifting task using wearable IMUs

Low back pain is commonly reported in occupational settings due to factors such as heavy lifting and poor ergonomic practices, often resulting in significant healthcare expenses and lowered productivity. Assessment tools for human motion and ergonomic risk at the workplace are still limited. Therefore, this study aimed to assess lower back muscle and joint reaction forces in laboratory conditions using wearable inertial measurement units (IMUs) during weight lifting, a frequently high-risk workplace task. Ten able-bodied participants were instructed to lift a 28 lbs. box while surface electromyography sensors, IMUs, and a camera-based motion capture system recorded their muscle activity and body motion. The data recorded by IMUs and motion capture system were used to estimate lower back muscle and joint reaction forces via musculoskeletal modeling. Lower back muscle patterns matched well with electromyography recordings. The normalized mean absolute differences between muscle forces estimated based on measurements of IMUs and cameras were less than 25 %, and the statistical parametric mapping results indicated no significant difference between the forces estimated by both systems. However, abrupt changes in motion, such as lifting initiation, led to significant differences (p < 0.05) between the muscle forces. Furthermore, the maximum L5-S1 joint reaction force estimated using IMU data was significantly lower (p < 0.05) than those estimated by cameras during weight lifting and lowering. The study showed how kinematic errors from IMUs propagated through the musculoskeletal model and affected the estimations of muscle forces and joint reaction forces. Our findings showed the potential of IMUs for in-field ergonomic risk evaluations.

Mechanical environment influences muscle activity during infant rolling

An infant's musculoskeletal and motor development is largely affected by their environment. Understanding how different mechanical environments affect an infant's movements and muscle use is necessary to inform the juvenile products industry and reduce incidents involving inclined nursery products each year. The purpose of this study was to determine how the coordinated movements and corresponding muscle activation patterns are affected by different mechanical environments, specifically the back incline angle. Thirty-eight healthy infants (age: 6.5 ± 0.7 months; 23 M/15 F) were enrolled in this IRB-approved in-vivo biomechanics study. Surface electromyography sensors recorded muscle activity of the erector spinae, abdominal muscles, quadriceps, and hamstrings while infants rolled in five different mechanical environments: a flat surface and four device configurations representing a range of inclines infants are commonly exposed to. Coordinated movements were determined using video. In all configurations featuring an inclined seatback angle, infants experienced significantly higher erector spinae muscle activation and significantly lower abdominal muscle activation compared to the flat surface. Infants also exhibited a different coordinated movement featuring spinal extension and a pelvic thrust in the inclined device configurations that was not previously observed on the flat surface alone. Understanding how infants coordinate their movements and use their muscles during rolling in different inclined environments provides more insight into motor development and may inform the juvenile products industry. Many factors impact an infant's movements, therefore future work should explore how other environmental interactions influence an infant's movements and muscle activation, particularly for rolling.

Hand gesture recognition with deep residual network using Semg signal.

To design and develop a classifier, named Sewing Driving Training based Optimization-Deep Residual Network (SDTO_DRN) for hand gesture recognition. The electrical activity of forearm muscles generates the signals that can be captured with Surface Electromyography (sEMG) sensors and includes meaningful data for decoding both muscle actions and hand movement. This research develops an efficacious scheme for hand gesture recognition using SDTO_DRN. Here, signal pre-processing is done through Gaussian filtering. Thereafter, desired and appropriate features are extracted. Following that, effective features are chosen using SDTO. At last, hand gesture identification is accomplished based on DRN and this network is effectively fine-tuned by SDTO, which is a combination of Sewing Training Based Optimization (STBO) and Driving Training Based Optimization (DTBO). The datasets employed for the implementation of this work are MyoUP Dataset and putEMG: sEMG Gesture and Force Recognition Dataset. The designed SDTO_DRN model has gained superior performance with magnificent results by delivering a maximum accuracy of 0.943, True Positive Rate (TPR) of 0.929, True Negative Rate (TNR) of 0.919, Positive Predictive Value (PPV) of 0.924, and Negative Predictive Value (NPV) of 0.924. The hand gesture recognition using the proposed model is accurate and improves the effectiveness of the recognition.

Impact of Ergonomics on Muscle Fatigue During Surgical Drilling Using Surface Electromyography.

To investigate the relationship between ergonomic positions and electromyographic muscle activity during otologic drilling. Cross-over experimental trial. Tertiary Academic Medical Center. Surgeon participants were tasked with delicate eggshell drilling in 3 different seated positions: "neutral,""slouched,"and "craned." Surface electromyography (sEMG) sensors recorded the amplitude and frequency of muscle activity. The joint analysis of spectrum and amplitude (JASA) method, which combines temporal trends in frequency and amplitude, was used to identify trials that exhibited patterns of fatigue. The sEMG amplitude and frequency responses demonstrated wide temporal changes. In a majority of experiments, amplitude increased over the course of the experiment, while frequency remained more stable. On analysis of variance testing, only the mean frequency of the deltoid differed significantly between postures (P = .02). Under the JASA framework, external carpi radialis and upper trapezius experienced fatigue in nearly half of the trials regardless of position (47% vs 49%). The upper trapezius demonstrated fatigue during 46% and 69% of the "craned" and "slouched" trials, respectively, compared to just 31% of the "neutral" trials. Fewer attendings demonstrated upper trapezius fatigue compared to trainees (33% vs 62%). Female surgeons experienced fatigue in more trials than male counterparts (73% vs 25%). This study highlights a first step in quantifying the relationship between operating postures and muscle fatigue. Results suggest that specific muscle groups are more susceptible to fatigue; gender and experience may also impact muscle activity.

Detecting Psychological Interventions Using Bilateral Electromyographic Wearable Sensors.

This study investigated the impact of auditory stimuli on muscular activation patterns using wearable surface electromyography (EMG) sensors. Employing four key muscles (Sternocleidomastoid Muscle (SCM), Cervical Erector Muscle (CEM), Quadricep Muscles (QMs), and Tibialis Muscle (TM)) and time domain features, we differentiated the effects of four interventions: silence, music, positive reinforcement, and negative reinforcement. The results demonstrated distinct muscle responses to the interventions, with the SCM and CEM being the most sensitive to changes and the TM being the most active and stimulus dependent. Post hoc analyses revealed significant intervention-specific activations in the CEM and TM for specific time points and intervention pairs, suggesting dynamic modulation and time-dependent integration. Multi-feature analysis identified both statistical and Hjorth features as potent discriminators, reflecting diverse adaptations in muscle recruitment, activation intensity, control, and signal dynamics. These features hold promise as potential biomarkers for monitoring muscle function in various clinical and research applications. Finally, muscle-specific Random Forest classification achieved the highest accuracy and Area Under the ROC Curve for the TM, indicating its potential for differentiating interventions with high precision. This study paves the way for personalized neuroadaptive interventions in rehabilitation, sports science, ergonomics, and healthcare by exploiting the diverse and dynamic landscape of muscle responses to auditory stimuli.

Assessment of Self-report, Palpation, and Surface Electromyography Dataset During Isometric Muscle Contraction

Measuring muscle fatigue involves assessing various components within the motor system. While subjective and sensor-based measures have been proposed, a comprehensive comparison of these assessment measures is currently lacking. This study aims to bridge this gap by utilizing three commonly used measures: participant self-reported perceived muscle fatigue scores, a sports physiotherapist’s manual palpation-based muscle tightness scores, and surface electromyography sensors. Compensatory muscle fatigue occurs when one muscle group becomes fatigued, leading to the involvement and subsequent fatigue of other muscles as they compensate for the workload. The evaluation of compensatory muscle fatigue focuses on nine different upper body muscles selected by the sports physiotherapist. With a cohort of 30 male subjects, this study provides a valuable dataset for researchers and healthcare practitioners in sports science, rehabilitation, and human performance. It enables the exploration and comparison of diverse methods for evaluating different muscles in isometric contraction.

A Simulated Intelligent Pixelated Electrode Array for Surface Electromyography Sensors

A Simulated Intelligent Pixelated Electrode Array for Surface Electromyography Sensors

Wearable Submental sEMG Sensors for the Treatment of Dysphagia; A Pilot Study

Swallowing is a complex physiological process that is essential for survival and quality of life. When disrupted, it leads to oropharyngeal dysphagia. This is a common affliction which can have massive implications on health, including an increased risk for pneumonia and malnutrition.&#x0D; Rehabilitative therapy is effective in improving swallowing function, but usually requires clinic visits with a speech language pathologist weekly or multiple times per week. Making treatment more accessible could improve follow-through among the disadvantaged patients with the greatest need for therapy.&#x0D; To address this need, the Purdue I-EaT lab is developing a wearable submental surface electromyography (sEMG) sensor system (i-Phagia) for remote treatment of dysphagia. To evaluate the feasibility of using this device for a single-visit treatment, a pilot pre-post study is being conducted. Participants are evaluated with videofluoroscopic swallowing studies (VFSS) and sEMG before and after a 45-minute treatment session, involving two rehabilitative swallowing exercises, using i-Phagia. Following the post-treatment evaluation, feedback is collected regarding the tolerability of the sensor, including presence of any skin irritation. Outcome variables include normalized mean amplitude, time to peak amplitude of the sEMG signal, and patient satisfaction and safety.&#x0D; As of now, five participants (ages 21, 21, 71, 69, 80) have participated. sEMG analysis was completed in four participants due to data loss for one. Pre-post sEMG analysis of neuromuscular activity showed an increase in normalized amplitude post-treatment in three of four analyzed participants. Four of five participants reported high satisfaction (average score 88/100) and one moderate, and all reported no pain (Wong-Baker pain scale).&#x0D; These results show the feasibility and preliminary effectiveness of using the i-Phagia for in-clinic treatment visits in a small sample of healthy participants. Further research is ongoing to examine the feasibility for use in patients with Parkinson’s disease and for remote use outside of clinic.

Continuous A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Kinematics Across Different Ambulation Tasks.

Volitional control systems for powered prostheses require the detection of user intent to operate in real life scenarios. Ambulation mode classification has been proposed to address this issue. However, these approaches introduce discrete labels to the otherwise continuous task that is ambulation. An alternative approach is to provide users with direct, voluntary control of the powered prosthesis motion. Surface electromyography (EMG) sensors have been proposed for this task, but poor signal-to-noise ratios and crosstalk from neighboring muscles limit performance. B-mode ultrasound can address some of these issues at the cost of reduced clinical viability due to the substantial increase in size, weight, and cost. Thus, there is an unmet need for a lightweight, portable neural system that can effectively detect the movement intention of individuals with lower-limb amputation. In this study, we show that a small and lightweight A-mode ultrasound system can continuously predict prosthesis joint kinematics in seven individuals with transfemoral amputation across different ambulation tasks. Features from the A-mode ultrasound signals were mapped to the user's prosthesis kinematics via an artificial neural network. Predictions on testing ambulation circuit trials resulted in a mean normalized RMSE across different ambulation modes of 8.7 ± 3.1%, 4.6 ± 2.5%, 7.2 ± 1.8%, and 4.6 ± 2.4% for knee position, knee velocity, ankle position, and ankle velocity, respectively. This study lays the foundation for future applications of A-mode ultrasound for volitional control of powered prostheses during a variety of daily ambulation tasks.

Muscle activation and coordinated movements of infant rolling

Rolling is a critical step of infant development, encouraging muscle coordination and enabling independent exploration. Understanding muscle activity during infant rolling movements on a flat surface is necessary to more fully characterize how the rolling milestone is achieved. The purpose of this study was to determine infants’ muscle activation throughout roll initiation for six previously established coordinated movements. Thirty-eight healthy infants (age: 6.5 ± 0.7 months; 23M/15F) were enrolled in this IRB-approved in-vivo biomechanics study. Surface electromyography sensors recorded muscle utilization from the erector spinae, abdominal muscles, quadriceps, and hamstrings while infants rolled. Each rolling movement was categorized as one of six roll types, and the mean muscle activity was analyzed. All roll types required initial activation of all measured muscle groups. Movements featuring axial rotation of the torso relative to the pelvis required highly active erector spinae muscles. Movements featuring trunk and hip flexion required highly active abdominal muscles. Infants used distinct coordinated muscle activations to achieve the six different roll types on a flat surface. A foundational understanding of the different muscle activation patterns required during infant rolling will provide crucial insight into motor development. This study quantified muscle coordination required of infants to achieve rolling on a firm flat surface. Previous research indicates that the mechanical environment in which an infant is placed impacts muscle activity and body position during normal lying. Therefore, future work should explore if mechanical environments that differ from a flat and firm surface also influence these coordinated movements and muscle activations.

Empowering human-robot interaction using sEMG sensor: Hybrid deep learning model for accurate hand gesture recognition

In this paper, a novel approach using a Henry Gas Solubility-based Stacked Convolutional Neural Network (HGS-SCNN) for hand gesture recognition using surface electromyography (sEMG) sensors is proposed. The stacked architecture of the CNN model helps to capture both low-level and high-level features, enabling effective representation learning. To begin, we generated a dataset comprising 600 samples of hand gestures. Next, we applied the Discrete Wavelet Transform (DWT) technique to extract features from the filtered sEMG signal. This step allowed us to capture both spatial and frequency information, enhancing the discriminative power of the extracted features. Extensive experiments are conducted to evaluate the performance of the proposed HGS-SCNN model. In addition, the obtained results are compared with state-of-the-art techniques, namely AOA-SCNN, GWO-SCNN, and WOA-SCNN. The comparative analysis demonstrates that the HGS-SCNN outperforms these existing methods, achieving an impressive accuracy of 99.3%. The experimental results validate the effectiveness of our proposed approach in accurately detecting hand gestures. The combination of DWT-based feature extraction and the HGS-SCNN model offers robust and reliable hand gesture recognition, thereby opening new possibilities for intuitive human-machine interaction and applications requiring gesture-based control.

Analysis of Musculoskeletal Biomechanics of Lower Limbs of Drivers in Pedal-Operation States

In this study, to establish the biomechanical characteristics of commercial vehicle drivers’ muscles and bones while operating the three pedals, a driver pedal-operation simulator was built, and the real-life situation was reconstructed in OpenSim 3.3 software. We set up three seat heights to investigate the drivers’ lower limbs, and the research proceeded in two parts: experiment and simulation. Chinese adult males in the 95th percentile were selected as the research participants. In the experiment, Delsys wireless surface electromyography (EMG) sensors were used to collect the EMG signals of the four main muscle groups of the lower limbs when the drivers operated the three pedals. Then, we analyzed the muscle activation and the degree of muscle fatigue. The simulation was based on OpenSim software to analyze the driver’s lower limb joint angles and joint torque. The results show that the activation of the hamstrings, gastrocnemius, and rectus femoris muscles were higher in the four muscle groups. In respect of torque, in most cases, hip joint torque &gt; knee joint torque &gt; ankle joint torque. The knee joint angles were the largest, and the ankle joint angles changed the most. The experimental results provide a reference for improving drivers’ handling comfort in commercial vehicles and provide theoretical bases for cab design and layout optimization.

Visualizing Hand Force With Wearable Muscle Sensing for Enhanced Mixed Reality Remote Collaboration.

In this paper, we present a prototype system for sharing a user's hand force in mixed reality (MR) remote collaboration on physical tasks, where hand force is estimated using wearable surface electromyography (sEMG) sensor. In a remote collaboration between a worker and an expert, hand activity plays a crucial role. However, the force exerted by the worker's hand has not been extensively investigated. Our sEMG-based system reliably captures the worker's hand force during physical tasks and conveys this information to the expert through hand force visualization, overlaid on the worker's view or on the worker's avatar. A user study was conducted to evaluate the impact of visualizing a worker's hand force on collaboration, employing three distinct visualization methods across two view modes. Our findings demonstrate that sensing and sharing hand force in MR remote collaboration improves the expert's awareness of the worker's task, significantly enhances the expert's perception of the collaborator's hand force and the weight of the interacting object, and promotes a heightened sense of social presence for the expert. Based on the findings, we provide design implications for future mixed reality remote collaboration systems that incorporate hand force sensing and visualization.

Detecting negative myoclonus during long-term home measurements using wearables

ObjectiveThe aim of this study was to develop a feasible method for the detection of negative myoclonus (NM) through long-term home measurements in patients with progressive myoclonus epilepsy type 1. MethodsThe number and duration of silent periods (SP) associated with NM were detected during a 48 h home recording using wearable surface electromyography (EMG) sensors. ResultsA newly developed algorithm was able to find short (50–69 ms), intermediate (70–100 ms), and long (101– 500 ms) SPs from EMG data. Negative myoclonus assessed by the algorithm correlated significantly with the video-recorded and physician-evaluated unified myoclonus rating scale (UMRS) scores of NM and action myoclonus. Silent period duration, number, and their combination, correlated strongly and significantly also with the Singer score, which assesses functional status and ambulation. ConclusionsNegative myoclonus can be determined objectively using long-term EMG measurements in home environment. With long-term measurements, we can acquire more reliable quantified information about NM as a symptom, compared to short evaluation at the clinic. SignificanceAs measured using SPs, NM may be a clinically useful measure for monitoring disease progression or assessing antimyoclonic drug effects objectively.

Imperceptive and reusable dermal surface EMG for lower extremity neuro-prosthetic control and clinical assessment

Surface electromyography (sEMG) sensors play a critical role in diagnosing muscle conditions and enabling prosthetic device control, especially for lower extremity robotic legs. However, challenges arise when utilizing such sensors on residual limbs within a silicon liner worn by amputees, where dynamic pressure, narrow space, and perspiration can negatively affect sensor performance. Existing commercial sEMG sensors and newly developed sensors are unsuitable due to size and thickness, or susceptible to damage in this environment. In this paper, our sEMG sensors are tailored for amputees wearing sockets, prioritizing breathability, durability, and reliable recording performance. By employing porous PDMS and Silbione substrates, our design achieves exceptional permeability and adhesive properties. The serpentine electrode pattern and design are optimized to improve stretchability, durability, and effective contact area, resulting in a higher signal-to-noise ratio (SNR) than conventional electrodes. Notably, our proposed sensors wirelessly enable to control of a robotic leg for amputees, demonstrating its practical feasibility and expecting to drive forward neuro-prosthetic control in the clinical research field near future.

What differences exist between the lead and trail wrist in extensor carpi ulnaris activity and golf swing joint kinematics in sub-elite golfers?

ABSTRACT This study assessed the lead and trail arm peak and average extensor carpi ulnaris (ECU) muscle activity in association with tri-planar angular velocities of the lead and trail wrists during the golf swing. Fifteen sub-elite, male right-handed golfers (M age = 34.7 years ±13.3, M handicap = 1.5 ± 2.2) were recruited to execute five shots each with their pitching wedge, 7-iron and driver clubs in an indoor golf simulator. Surface electromyography (EMG) sensors were placed over the ECU muscle belly and inertial measurement unit sensors were placed bi-laterally on the distal forearm and dorsum of the hand. There was a statistically greater recruitment of the trail ECU muscle during the downswing (p < 0.001) for all clubs. The lead ECU muscle was recruited more during the backswing (p < 0.001) and follow through (p < 0.024) phases. There were statistically different tri-planar movement patterns between the lead and trail wrist throughout all three phases of the golf swing. No significant relationships were found between downswing EMG data and clubhead kinematics at impact. In conclusion, differing wrist kinematics and associated muscle activity may contribute to the asymmetrical injury pattern seen clinically.

Analysis of Comfort during Transfer by a Dual-Arm Care Robot Based on Human Body Pressure and Surface Electromyographic Signals.

In-home elderly care faces a crucial challenge regarding mobility among essential assistive devices, for which dual-arm care robots present a viable solution. However, ensuring human comfort in human-robot interactions necessitate quantifiable standards. Currently, the field lacks accurate biomechanical model solutions and objective comfort evaluation. In response to this need, this study proposes a method for solving human-robot statics models based on real-time pressure and position information. Employing the Optitrack motion capture system and Tekscan pressure sensors, we collect real-time positional and pressure data. This information is then incorporated into our human-robot statics model, facilitating the instantaneous calculation of forces and moments within the human body's sagittal plane. Building on this, comprehensive research literature review and meticulous questionnaire surveys are conducted to establish a comprehensive comfort evaluation function. To validate this function, experiments are performed to enable real-time assessment of comfort levels experienced during the process of transferring the human body. Additionally, the Noraxon surface electromyography (sEMG) sensors are utilized to capture real-time sEMG signals from the erector spinae, adductor muscles and quadratus lumborum, thereby providing objective validation for the comfort evaluation function. The experimental findings demonstrate that the proposed methodology for evaluating comfort achieves an accuracy rate of 85.1%.