Surface Electromyography Electrodes Research Articles

Electromyographic Analysis of Upper- and Lower-extremity Muscles in Adults during Agro-healing Activities

This study investigated the activity of upper- and lower-extremity muscles for 15 agricultural tasks of agro-healing. For the development of an agro-healing program using farm resource types, 15 selected agro-healing activities (namely, digging, raking, fertilizing, planting transplants, tying plants to stakes, watering, harvesting, washing, cutting, cooking, collecting natural objects, decorating natural objects, interacting with dogs, walking dogs, and feeding fish) were extracted and performed in a total of 21 adults (average age: 42.29 ± 14.76 years) at D Care Farm in Cheongju, Korea, from June to July 2022. Before these activities, informed consent was obtained from participants and muscle activity of the upper and lower extremities was measured. Muscle activation during activity performance was measured using electromyography (EMG), and the rating of perceived exertion for each activity was investigated. Bipolar surface EMG electrodes were attached at 16 locations on the left and right upper-extremity muscles (anterior deltoid, biceps brachialis, brachioradialis, and flexor carpi ulnaris) and lower-extremity muscles (vastus lateralis, vastus medialis, biceps femoris, and gastrocnemius). The results indicated that the activity of the lower-extremity muscles was higher than that of the upper-extremity muscles during 15 agricultural activities. During plant-mediated activity and animal-assisted activities, the rate of right muscle use was higher than that of left muscle use among the upper-extremity muscles, whereas the rate of right and left muscle use showed a similar tendency among the lower-extremity muscles. During plant-mediated activities, agricultural activities involving the use of heavy tools highly activated the right forearm muscle (flexor carpi ulnaris), whereas holding and interacting with animals highly activated the left forearm muscles (biceps brachialis, brachioradialis, and flexor carpi ulnaris). It is expected that the EMG data obtained in this study can be used as basic biomechanical data when designing an agro-healing program to improve physical function.

Evaluation of Hand Action Classification Performance Using Machine Learning Based on Signals from Two sEMG Electrodes.

Classification-based myoelectric control has attracted significant interest in recent years, leading to prosthetic hands with advanced functionality, such as multi-grip hands. Thus far, high classification accuracies have been achieved by increasing the number of surface electromyography (sEMG) electrodes or adding other sensing mechanisms. While many prescribed myoelectric hands still adopt two-electrode sEMG systems, detailed studies on signal processing and classification performance are still lacking. In this study, nine able-bodied participants were recruited to perform six typical hand actions, from which sEMG signals from two electrodes were acquired using a Delsys Trigno Research+ acquisition system. Signal processing and machine learning algorithms, specifically, linear discriminant analysis (LDA), k-nearest neighbors (KNN), and support vector machines (SVM), were used to study classification accuracies. Overall classification accuracy of 93 ± 2%, action-specific accuracy of 97 ± 2%, and F1-score of 87 ± 7% were achieved, which are comparable with those reported from multi-electrode systems. The highest accuracies were achieved using SVM algorithm compared to LDA and KNN algorithms. A logarithmic relationship between classification accuracy and number of features was revealed, which plateaued at five features. These comprehensive findings may potentially contribute to signal processing and machine learning strategies for commonly prescribed myoelectric hand systems with two sEMG electrodes to further improve functionality.

Research Progress in Data Acquisition and Intelligent Sensing Methods for Lumbar Electromyographic Signals

Population aging trend is taking place in our country, and low back pain is a symptom of neuromuscular diseases of concern in the elderly. Accurately analyzing the disease of low back pain is important for both timely intervention and rehabilitation of patients. As a kind of bioelectrical signal, the acquisition and analysis of lumbar electromyography (EMG) signal is an important direction for the study of low back pain. The study reviews the acquisition of lumbar EMG by different types of sensors, introduces the signal characteristics of needle electrodes, surface electromyography electrodes and array electrodes, describes the use of signal algorithms, points out that wireless sensors and the use of deep learning algorithms are the direction of development, and puts forward prospects for its further development.

Central and Peripheral Motor Conduction Studies by Single-Pulse Magnetic Stimulation.

Single-pulse magnetic stimulation is the simplest type of transcranial magnetic stimulation (TMS). Muscle action potentials induced by applying TMS over the primary motor cortex are recorded with surface electromyography electrodes, and they are called motor-evoked potentials (MEPs). The amplitude and latency of MEPs are used for various analyses in clinical practice and research. The most commonly used parameter is the central motor conduction time (CMCT), which is measured using motor cortical and spinal nerve stimulation. In addition, stimulation at the foramen magnum or the conus medullaris can be combined with conventional CMCT measurements to evaluate various conduction parameters in the corticospinal tract more precisely, including the cortical-brainstem conduction time, brainstem-root conduction time, cortical-conus motor conduction time, and cauda equina conduction time. The cortical silent period is also a useful parameter for evaluating cortical excitability. Single-pulse magnetic stimulation is further used to analyze not only the central nervous system but also the peripheral nervous system, such as for detecting lesions in the proximal parts of peripheral nerves. In this review article we introduce four types of single-pulse magnetic stimulation-of the motor cortex, spinal nerve, foramen magnum, and conus medullaris-that are useful for the diagnosis, elucidation of pathophysiology, and evaluation of clinical conditions and therapeutic effects. Single-pulse magnetic stimulation is a clinically useful technique that all neurologists should learn.

All 3D‐Printed Soft High‐Density Surface Electromyography Electrode Arrays for Accurate Muscle Activation Mapping and Decomposition

AbstractHigh‐density surface electromyography (sEMG) electrode arrays enable the recording of tens to hundreds of channels of electromyographic signals, which have found wide applications in clinics and human‐machine interfaces. However, current manufacturing technologies of high‐density sEMG electrode arrays generally involve high‐cost equipments, complicated procedures, and insufficient programmability, severely hampering the rational design and practical applications of customized yet cost‐effective high‐density electrode arrays. Herein, the facile and efficient fabrication of novel 32‐channel soft high‐density sEMG electrode arrays by an all‐printed technique based on multimaterial direct ink writing 3D printing is presented. By employing rational four‐layer stacked structure designs with systematic ink printability evaluation, it can successfully realize seamless interfacial integration during the multimaterial printing, achieving reproducible, programmable, continuous fabrication of soft high‐density sEMG electrode arrays. The all 3D‐printed soft electrode arrays exhibit excellent stability, low impedance, and high signal‐to‐noise ratio superior to commercial products with an increase of 32.2%. Such intriguing properties enable this all 3D‐printed electrode arrays the unique capability of mapping muscle activation of the forearm, and the motor unit action potential trains can be precisely identified for varying hand gestures to effectively explore the innovative human‐machine interface toward diverse applications such as teleoperation and prosthetic control.

Impact of Starting Knee Flexion Angle on Muscle Activity and Performance during Plyometrics without Jumping.

Most of the existing research has focused on jump plyometrics, where landing reaction forces must be dissipated among lower limb articulations. In contrast, the investigation of resisted plyometrics without jumping, devoid of such landing forces, remains relatively limited. This study aimed to (i) investigate the impact of resisted plyometrics without jumping at two knee flexion angles (60 and 90 degrees) on vastus muscle activity relative to limb dominance and (ii) assess strength, power, and work during the concentric-eccentric phases of these exercises. Thirty-one healthy participants underwent quantification of lower limb muscle amplitude, strength, power, and work during resisted plyometrics without jumping from both 60° and 90° knee flexion positions. After anthropometric evaluations, participants used a dynamometer with a load equal to 80% of body weight while wireless surface electromyography electrodes recorded data. Statistical analyses utilized paired t-tests or nonparametric equivalents and set significance at p ≤ 0.05. Results showed significantly higher muscle activity in the vastus medialis (VM) (dominant: 47.4%, p = 0.0008, rs = 0.90; nondominant: 54.8%, p = 0.047, rs = 0.88) and vastus lateralis (VL) (dominant: 46.9%, p = 0.0004, rs = 0.86; nondominant: 48.1%, p = 0.021, rs = 0.67) muscles when exercises started at 90° knee flexion, regardless of limb dominance. Substantial intermuscle differences occurred at both 60° (50.4%, p = 0.003, rs = 0.56) and 90° (54.8%, p = 0.005, rs = 0.62) knee flexion, favoring VM in the nondominant leg. Concentric and eccentric strength, power, and work metrics significantly increased when initiating exercises from a 90° position. In conclusion, commencing resisted plyometrics without jumping at a 90° knee flexion position increases VM and VL muscle activity, regardless of limb dominance. Furthermore, it enhances strength, power, and work, emphasizing the importance of knee flexion position customization for optimizing muscle engagement and functional performance.

Spatial Feature Integration in Multidimensional Electromyography Analysis for Hand Gesture Recognition

Enhancing information representation in electromyography (EMG) signals is pivotal for interpreting human movement intentions. Traditional methods often concentrate on specific aspects of EMG signals, such as the time or frequency domains, while overlooking spatial features and hidden human motion information that exist across EMG channels. In response, we introduce an innovative approach that integrates multiple feature domains, including time, frequency, and spatial characteristics. By considering the spatial distribution of surface electromyographic electrodes, our method deciphers human movement intentions from a multidimensional perspective, resulting in significantly enhanced gesture recognition accuracy. Our approach employs a divide-and-conquer strategy to reveal connections between different muscle regions and specific gestures. Initially, we establish a microscopic viewpoint by extracting time-domain and frequency-domain features from individual EMG signal channels. We subsequently introduce a macroscopic perspective and incorporate spatial feature information by constructing an inter-channel electromyographic signal covariance matrix to uncover potential spatial features and human motion information. This dynamic fusion of features from multiple dimensions enables our approach to provide comprehensive insights into movement intentions. Furthermore, we introduce the space-to-space (SPS) framework to extend the myoelectric signal channel space, unleashing potential spatial information within and between channels. To validate our method, we conduct extensive experiments using the Ninapro DB4, Ninapro DB5, BioPatRec DB1, BioPatRec DB2, BioPatRec DB3, and Mendeley Data datasets. We systematically explore different combinations of feature extraction techniques. After combining multi-feature fusion with spatial features, the recognition performance of the ANN classifier on the six datasets improved by 2.53%, 2.15%, 1.15%, 1.77%, 1.24%, and 4.73%, respectively, compared to a single fusion approach in the time and frequency domains. Our results confirm the substantial benefits of our fusion approach, emphasizing the pivotal role of spatial feature information in the feature extraction process. This study provides a new way for surface electromyography-based gesture recognition through the fusion of multi-view features.

Neuromechanics of finger hangs with arm lock-offs: analyzing joint moments and muscle activations to improve practice guidelines for climbing.

Climbing imposes substantial demands on the upper limbs and understanding the mechanical loads experienced by the joints during climbing movements is crucial for injury prevention and optimizing training protocols. This study aimed to quantify and compare upper limb joint loads and muscle activations during isometric finger hanging exercises with different arm lock-off positions. Seventeen recreational climbers performed six finger dead hangs with arm lock-offs at 90° and 135° of elbow flexion, as well as arms fully extended. Upper limb joint moments were calculated using personalized models in OpenSim, based on three-dimensional motion capture data and forces measured on an instrumented hang board. Muscle activations of upper limb muscles were recorded with surface electromyography electrodes. Results revealed that the shoulder exhibited higher flexion moments during arm lock-offs at 90° compared to full extension (p = 0.006). The adduction moment was higher at 135° and 90° compared to full extension (p < 0.001), as well as the rotation moments (p < 0.001). The elbows exhibited increasing flexion moments with the increase in the arm lock-off angle (p < 0.001). Muscle activations varied across conditions for biceps brachii (p < 0.001), trapezius (p < 0.001), and latissimus dorsi, except for the finger flexors (p = 0.15). Our findings indicate that isometric finger dead hangs with arms fully extended are effective for training forearm force capacities while minimizing stress on the elbow and shoulder joints. These findings have important implications for injury prevention and optimizing training strategies in climbing.

Stretchable surface electromyography electrode array patch for tendon location and muscle injury prevention

Surface electromyography (sEMG) can provide multiplexed information about muscle performance. If current sEMG electrodes are stretchable, arrayed, and able to be used multiple times, they would offer adequate high-quality data for continuous monitoring. The lack of these properties delays the widespread use of sEMG in clinics and in everyday life. Here, we address these constraints by design of an adhesive dry electrode using tannic acid, polyvinyl alcohol, and PEDOT:PSS (TPP). The TPP electrode offers superior stretchability (~200%) and adhesiveness (0.58 N/cm) compared to current electrodes, ensuring stable and long-term contact with the skin for recording (>20 dB; >5 days). In addition, we developed a metal-polymer electrode array patch (MEAP) comprising liquid metal (LM) circuits and TPP electrodes. The MEAP demonstrated better conformability than commercial arrays, resulting in higher signal-to-noise ratio and more stable recordings during muscle movements. Manufactured using scalable screen-printing, these MEAPs feature a completely stretchable material and array architecture, enabling real-time monitoring of muscle stress, fatigue, and tendon displacement. Their potential to reduce muscle and tendon injuries and enhance performance in daily exercise and professional sports holds great promise.

Self-healing electrical bioadhesive interface for electrophysiology recording

Electrical bioadhesive interfaces (EBIs) are standing out in various applications, including medical diagnostics, prosthetic devices, rehabilitation, and human–machine interactions. Nonetheless, crafting a reliable and advanced EBI with comprehensive properties spanning electrochemical, electrical, mechanical, and self-healing capabilities remains a formidable challenge. Herein, we develop a self-healing EBI by thoughtfully integrating conducting polymer nanofibers and a typical bioadhesive within a robust hydrogel matrix. The accomplished EBI demonstrates extraordinary adhesion (lap shear strength of 197 kPa), exceptional electrical conductivity (2.18 S m−1), and outstanding self-healing performance. Taking advantage of these attributes, we integrated the EBI into flexible skin electrodes for surface electromyography (sEMG) signal recording from forearm muscles. The engineered skin electrodes exhibit robust adhesion to the skin even when sweating, rapid self-healing from damage, and seamless real-time signal recording with a higher signal-to-noise ratio (39 dB). Our EBI, along with its skin electrodes, offers a promising platform for tissue-device integration, health monitoring, and an array of bioelectronic applications.

A case-study comparison of qualitative and quantitative techniques for assessing surgeon ergonomics

Surgeons are often exposed to many ergonomic risk factors during open, laparoscopic, and robotic-assisted surgical procedures, which lead to pain or discomfort in alarmingly high percentages of surgeons. Ergonomic risk factors specific to colon and rectal surgery subspecialty have been hypothesized, but little research has been done on this topic. To investigate this, an ergonomics-centered case study was performed with an experienced colon and rectal surgeon at a teaching hospital to investigate their experiences with surgery, ergonomics, and musculoskeletal pain. Specifically, a semi-structured interview was performed with the participant, and skin surface electromyography electrodes were placed on the participant to record muscle activity data as they performed rectal surgeries using open, laparoscopic, and robotic-assisted surgical techniques. The results of this case study indicate that for this participant, robotic surgery may provide the most relief from current pain and discomfort; however, the relationship between muscle activation and surgical modality is complex, and both qualitative and quantitative methods are needed to provide a complete ergonomic analysis of colon and rectal surgery in a teaching hospital setting.

The Effect of Volitional Preemptive Abdominal Contraction on Biomechanical Measures During A Front Versus Back Loaded Barbell Squat.

Weightlifting is growing in popularity among recreational and competitive athletes. The barbell back squat (BackS) is commonly included in these training programs, while the barbell front squat (FrontS) is commonly performed as a component of other lifts such as the power clean or clean and jerk, it is less commonly practiced in isolation. The purpose of this study was to examine the effects of VPAC performance on trunk muscle and LE biomechanical responses during loaded BackS versus FrontS in healthy subjects. Controlled Laboratory Study. Healthy male subjects with the ability to perform a sub-maximal loaded barbell squat lift were recruited. Subjects completed informed consent, demographic/medical history questionnaires and an instructional video. Subjects practiced VPAC and received feedback. Surface electromyography (sEMG) electrodes and kinematic markers were applied. Muscles included were the internal oblique (IO), external oblique (EO), rectus abdominis, iliocostalis lumborum (ICL), superficial multifidi, rectus femoris, vastus lateralis, biceps femoris, and gluteus maximus. Maximal voluntary isometric contractions established reference sEMG values. A squat one-rep-max (1RM) was predicted by researchers using a three to five repetition maximum (3RM, 5RM) load protocol. Subjects performed BackS trials at 75% 1RM while FrontS trials were performed at 75% BackS weight, both with and without VPAC. Subjects performed three repetitions of each condition with feet positioned on two adjacent force plates. Significant interactions and main effects were tested using a 2(VPAC strategy) x 2(squat variation) and 2(VPAC strategy) x 2(direction) within-subject repeated measures ANOVAs. Tukey's Post-Hoc tests identified the location of significant differences. Trunk muscle activity was significantly higher during FrontS versus BackS regardless of VPAC condition. (IO: p=0.018, EO: p<0.001, ICL: p<0.001) VPAC increased performance time for both squat variations (p=.0011), which may be associated with decreased detrimental force potential on the lumbar spine and knees. VPAC led to improved ability to maintain a neutral lumbar spine during both squat variations. This finding is associated with decreased detrimental force potential on the lumbar spine. Findings could help guide practitioners and coaches to choose squat variations and incorporate VPAC strategies during their treatments and/or training programs. Level 3©The Author(s).

A Multi-Modal Under-Sensorized Wearable System for Optimal Kinematic and Muscular Tracking of Human Upper Limb Motion.

Wearable sensing solutions have emerged as a promising paradigm for monitoring human musculoskeletal state in an unobtrusive way. To increase the deployability of these systems, considerations related to cost reduction and enhanced form factor and wearability tend to discourage the number of sensors in use. In our previous work, we provided a theoretical solution to the problem of jointly reconstructing the entire muscular-kinematic state of the upper limb, when only a limited amount of optimally retrieved sensory data are available. However, the effective implementation of these methods in a physical, under-sensorized wearable has never been attempted before. In this work, we propose to bridge this gap by presenting an under-sensorized system based on inertial measurement units (IMUs) and surface electromyography (sEMG) electrodes for the reconstruction of the upper limb musculoskeletal state, focusing on the minimization of the sensors' number. We found that, relying on two IMUs only and eight sEMG sensors, we can conjointly reconstruct all 17 degrees of freedom (five joints, twelve muscles) of the upper limb musculoskeletal state, yielding a median normalized RMS error of 8.5% on the non-measured joints and 2.5% on the non-measured muscles.

Using Wavelet Analysis and Deep Learning for EMG-Based Hand Movement Signal Classification

In this study; time series electromyography (EMG) data have been classified according to hand movements using wavelet analysis and deep learning. A pre-trained deep CNN (Convolitonal Neural Network-GoogLeNet) has been used in the classification process performed with signal processing, by this way the results can be obtained by continuous wavelet transform and classification methods. The dataset used has been taken from the Machine Learning Repository at the University of California. In the data set; EMG data of 5 healthy individuals, 2 males and 3 females, of the same age (~20-22 years) are available. Data; It consists of grasping spherical objects (Spher), grasping small objects with fingertips (Tip), grasping objects with palms (Palm), grasping thin/flat objects (Lat), grasping cylindrical objects (Cyl) and holding heavy objects (Hook). It is desired to perform 6 hand movements at the same time. While these movements are necessary, speed and power depend on one's will. People perform each movement for 6 seconds and repeat each movement (action) 30 times. The CWT (Continuous Wavelet Transform) method was used to transform the signal into an image. The scalogram image of the signal was created using the CWT method and the generated images were collected in a data set folder. The collected scalogram images have been classified using GoogLeNet, a deep learning network model. With GoogLeNet, results with 97.22% and 88.89% accuracy rates were obtained by classifying the scalogram images of the signals received separately from channel 1 and channel 2 in the data set. The applied model can be used to classify EMG signals in EMG data with high success rate. In this study, 80% of data was used for educational purposes and 20% for validation purposes. In the study, the results of the classification processes have been evaluated separately for first and second channel data.

Using Flexible and Stretchable Surface Electromyography Electrode Array to Evaluate Congenital Muscular Torticollis in Children.

Congenital Muscular Torticollis (CMT) is a neuromuscular disease in children, which leads to exacerbation of postural deformity and neck muscle dysfunction with age. Towards facilitating functional assessment of neuromuscular disease in children, topographic electromyography (EMG) maps enabled by flexible and stretchable surface EMG (sEMG) electrode arrays are used to evaluate the neck myoelectric activities in this study. Customed flexible and stretchable sEMG electrode arrays with 84 electrodes were utilized to record sEMG in all subjects during neck motion tasks. Clinical parameter assessments including the cervical range of motion (ROM), sonograms of the sternocleidomastoid (SCM), and corresponding histological analysis were also performed to evaluate the CMT. The muscle activation patterns of neck myoelectric activities between the CMT patients and the healthy subjects were asymmetric during different neck motion tasks. The CMT patients presented significantly lower values in spatial features of two-dimensional (2D) correlation coefficient, left/right energy ratio, and left/right energy difference (p < 0.001). The 2D correlation coefficient of activation patterns of neck rotation and extension in CMT patients significantly correlated with clinical parameter assessments (p < 0.05). The findings suggest that the spatial features of muscle activation patterns based on the sEMG electrode arrays can be utilized to evaluate the CMT. The flexible and stretchable sEMG electrode array is promising to facilitate the functional evaluation and treatment strategies for children with neuromuscular disease.

SEMG Signals Identification Using DT And LR Classifier by Wavelet-Based Features

In the recent era of technology, biomedical signals have been attracted lots of attention regarding the development of rehabilitation robotic technology. The surface electromyography (SEMG) signals are the fabulous signals utilized in the field of robotics. In this context, SEMG signals have been acquired by twenty-five right-hand dominated healthy human subjects to discriminate the various hand gestures. The placement of SEMG electrodes has been done according to the predefined acupressure point of required hand movements. After the SEMG signal acquisition, pre-processing and noise rejection have been performed. The de-noising and four levels of SEMG signal decomposition have been accomplished by discrete wavelet transform (DWT). In this article, the third and fourth-level detail coefficients have been utilized for time-scale feature extractions. The performance of ten time-scale features has been evaluated and compared to each other with the three-fold cross-validation technique by using a Decision Tree (DT) and Linear Regression (LR) classifier. The results demonstrated that the DT classifier classification accuracy was found superior to the LR classifier. By using the DT classifier technique 96.3% accuracy has been achieved, with all combined features as a feature vector.

Differences in muscle coordination between older men and women during brisk walking.

Brisk walking is a highly recommended physical activity for healthy community-dwelling older adults. The objective of this study was to examine through principal component analysis (PCA) how muscle coordination differs between older women and men during brisk walking. Thirty-five healthy older adults (65.2 ± 3.0 years old, 18 females, and 17 males) participated in the study. Eight surface electromyographic electrodes were used to record lower extremity muscle activities, and four inertial measurement unit sensors to monitor hip, knee, and ankle motion. Energy expenditure and heart rate were also measured during brisk walking. The effects of a person's sex on muscle coordination were identified through wavelet and PCA analysis of the sEMG signals. The results of energy expenditure and heart rate confirmed that brisk walking exercise is beneficial for older adults. PCA analysis showed that muscle coordination patterns differ between older women and men: during the stance phase a greater co-contraction of tibialis anterior and soleus in the men, and a greater activation of the quadriceps muscles during the loading-response phase in the women. The wavelet and PCA analyses facilitated a quantitative appraisal of sex-specific differences in the muscle coordination patterns of older men and women during brisk walking.

Accurate and Robust Locomotion Mode Recognition Using High-Density EMG Recordings from a Single Muscle Group.

Existing methods for human locomotion mode recognition often rely on using multiple bipolar electrode sensors on multiple muscle groups to accurately identify underlying motor activities. To avoid this complex setup and facilitate the translation of this technology, we introduce a single grid of high-density surface electromyography (HDsEMG) electrodes mounted on a single location (above the rectus femoris) to classify six locomotion modes in human walking. By employing a neural network, the trained model achieved average recognition accuracy of 97.7% with 160ms latency, significantly better than the model trained with one bipolar electrode pair placed on the same muscle (71.4% accuracy). To further exploit the spatial and temporal information of HDsEMG, we applied data augmentation to generate artificial data from simulated displaced electrodes, aiming to counteract the influence of electrode shifts. By employing a convolutional neural network with the enhanced dataset, the updated model was not strongly affected by electrode misplacement (93.9% accuracy) while models trained by bipolar electrode data were significantly disrupted by electrode shifts (29.4% accuracy). Findings suggest HDsEMG could be a valuable resource for mapping gait with fewer sensor locations and greater robustness. Results offer future promise for real-time control of assistive technology such as exoskeletons.

Verification of surface electromyographic activity of the oblique externus abdominis using ultrasound shear wave elastography.

This study used ultrasound shear wave elastography (SWE) to revalidate whether surface electromyographic (EMG) electrodes placed on the oblique externus abdominis (OE) can detect only the OE activity without the confounding activity of the underlying oblique internus abdominis (OI). During left and right trunk rotations, the EMG activity was acquired using surface EMG electrodes placed on the right OE. Shear wave velocity (Vs) values of the right OE and OI were acquired using SWE. The EMG activity during the left and right trunk rotations significantly increased as the level of exertion increased (25%, 50%, 75%, and 100% of the one‐repetition maximum [1RM]). The Vs of the right OE was significantly different only between 25% and 75% 1RM in the right trunk rotation, but significantly increased from 25% to 75% 1RM during the left trunk rotation. The Vs of the right OI during the right trunk rotation significantly increased with increased levels of exertion, except between 50% and 75% 1RM. The results for the Vs of the OE and OI in the right trunk rotation suggest that surface EMG electrodes placed on the OE would detect not only the antagonistic OE activity but also the agonistic OI activity.

Towards Dynamic Multi-Modal Intent Sensing Using Probabilistic Sensor Networks.

Intent sensing—the ability to sense what a user wants to happen—has many potential technological applications. Assistive medical devices, such as prosthetic limbs, could benefit from intent-based control systems, allowing for faster and more intuitive control. The accuracy of intent sensing could be improved by using multiple sensors sensing multiple environments. As users will typically pass through different sensing environments throughout the day, the system should be dynamic, with sensors dropping in and out as required. An intent-sensing algorithm that allows for this cannot rely on training from only a particular combination of sensors. It should allow any (dynamic) combination of sensors to be used. Therefore, the objective of this study is to develop and test a dynamic intent-sensing system under changing conditions. A method has been proposed that treats each sensor individually and combines them using Bayesian sensor fusion. This approach was tested on laboratory data obtained from subjects wearing Inertial Measurement Units and surface electromyography electrodes. The proposed algorithm was then used to classify functional reach activities and compare the performance to an established classifier (k-nearest-neighbours) in cases of simulated sensor dropouts. Results showed that the Bayesian sensor fusion algorithm was less affected as more sensors dropped out, supporting this intent-sensing approach as viable in dynamic real-world scenarios.