Electromyography Signals Research Articles

The study on the characteristics of active force of neck muscles under rapid braking conditions

This paper studies the active force characteristics of the neck muscles under the condition of rapid braking, which can provide theoretical support for reducing the neck injury of pilots when carrier-based aircraft blocks the landing. We carried out static loading and real vehicle braking experiments under rapid braking conditions, collected the active contraction force and electromyography (EMG) signals of neck muscles, and analyzed the response characteristics of neck muscle active force response. The results showed that the head and neck forward tilt time was delayed and the amplitude decreased during neck muscle pre-tightening. The duration of the neck in the extreme position decreased, and the recovery towards the seat direction was faster. The EMG signals of trapezius muscle was higher than sternocleidomastoid muscle. This suggests that pilots can reduce neck injury by pre-tightening the neck muscles during actual braking flight. In addition, we can consider the design of relevant fittings for pre-tightening the neck muscles.

PROGRAM FOR DETERMINING THE INFORMATIVE PARAMETERS OF SURFACE ELECTROMYOGRAPHIC SIGNALS

The article analyzes and calculates informative parameters of surface electromyographic signals (sEMS), which can be used to control biotechnical systems, as well as to diagnose the state of the musculoskeletal system. The analysis parameters in the time and frequency-time domains of the signal are considered. A program has been developed for calculating the informative indicators of the signal in the indicated areas. The program is implemented in the LabVIEW environment. To analyze the sEMG signal in the time domain, using the developed program, such indicators as Integral EMG, Average amplitude change, Wavelength, Simple quadratic integral, Absolute value of the 3rd time moment, and others were calculated; and to describe the signal spectrum by methods of time-frequency analysis, the average frequency of the spectrum (mean power frequency-MPF), the median frequency of the spectrum (median Frequency-MF), root mean square (RMS), power density spectrum (PDS), half width - the width of the spectrum at half maximum amplitude (HW). To test the program, files of real sEMQ signals were used. The calculated parameters of the sEMG analysis in the time and frequency-time domains make it possible to non-invasively and objectively assess the state of the musculoskeletal system. Keywords: Surface electromyographic signals, biotechnical systems, time-frequency analysis, Labvıew software.

Investigation of the intraoperative cortical responses to spinal motor mapping in a patient with chronic pain.

Intraoperative neurophysiological monitoring (IONM) in spinal cord stimulation (SCS) surgery for chronic pain is shown to provide effective guidance during device placement. Electromyography (EMG) is used to determine the laterality of the paddle. In some SCS cases, laterality cannot be obtained via EMG due to patient physiology. Electroencephalography (EEG) is already used in IONM to monitor cortical responses. Here, we show proof-of-concept of assessing the responses of epidurally evoked EMGs simultaneously with EEGs to determine laterality during IONM using a high-resolution (HR) SCS paddle. An 8-column HR-SCS paddle was acutely placed at T9-T10 interspace in patients with failed back surgery syndrome. EMG signals from 18 muscle groups were recorded simultaneously with 60-channel EEG signals at various stimulation amplitudes (0-10 mA). Particular attention was paid to regions associated with pain including the somatosensory cortex (S1), prefrontal cortex (PFC), and motor cortex (M1). When left and right lateral contacts were stimulated at low amplitudes (1-2 mA), significant changes were seen in θ, α, and β powers in the contralateral PFC but not in M1 or S1. There was a significant correlation between M1 and contralateral contacts in α power. At higher currents (7-8 mA), right-sided contacts resulted in α power change. We found significant differences in α, θ, and β powers in PFC for contralateral stimulation of the lateral SCS contacts at low amplitudes and in α power at higher amplitudes. The changes in PFC suggest the potential of EEG for understanding a cortical mechanism of action of SCS and provide insight into the pathophysiology of chronic pain.NEW & NOTEWORTHY Here, we present proof of concept of assessing the responses of epidurally evoked electromyography simultaneously with scalp electroencephalography to determine whether both laterality and insights into pain mechanisms can be elucidated. With stimulation, significant changes were seen in θ, α, and β band power in the contralateral prefrontal cortex and in α power in the motor cortex. We provide insight into the mechanism of action of SCS in preventing pain in this patient.

The TOFr of 0.75 to 0.85 is the optimal timing for IONM during thyroid surgery: a prospective observational cohort study

BackgroudRecurrent laryngeal nerve (RLN) injury is one of the serious complications of thyroid tumour surgery, surgical treatment of thyroid cancer requires careful consideration of the RLN and its impact on glottis function. There has been no unified standard for precise neuromuscular block monitoring to guide the monitoring of RLN in thyroid surgery. This study aimed to investigate the correlation between Train-of-four stabilization ratio (TOFr) and neural signal values of intraoperative neurophysiological monitoring (INOM) during thyroid operation, and further to determine the optimal timing for INOM during thyroid operation.MethodsPatients scheduled for thyroid tumour resection with INOM and RLN monitoring from April 2018 to July 2018 in our center were recruited. Electromyography (EMG) signals and corresponding TOFr were collected. All nerve stimulation data were included in group VR. Vagus nerve stimulation data were included in Subgroup V. RLN stimulation data were included in Subgroup R. The timing of recording was as follows: Vagus nerve EMG amplitude after opening the lateral space between the thyroid and carotid sheath and before the initiation of thyroid dissection, RLN EMG amplitude at first recognition, RLN EMG amplitude after complete thyroid dissection (Repeat three times), and Vagus nerve EMG amplitude after resection of the thyroid (Repeat three times). Correlation analysis of continuous variables was described by a scatter diagram. Pearson correlation analysis or Spearman correlation analysis was used for the two groups of variables.ResultsFinally, 134 vagus nerve signals and 143 RLN signals were analysed after matching with TOFr. The EMG amplitude in the VR group and subgroups after nerve stimulation was positively correlated with TOFr (p < 0.05). In the VR, V and R group, the incidence of EMG ≥ 500 µV in the 0.75 < TOFr ≤ 0.85 interval was significantly higher than the 0 < TOFr ≤ 0.75 interval (P = 0.002, P = 0.013 and P = 0.029), and has no statistical difference compared to 0.85 < TOFr ≤ 0.95 interval (P > 0.05).ConclusionsThe EMG signals of the RLN and vagus nerve stimulation during thyroid surgery were positively correlated with TOFr. TOFr > 0.75 could reflect more than 50% of the effective nerve electrophysiological signals, 0.75 < TOFr ≤ 0.85 interval was the optimal timing for IONM during thyroid surgery.Trial registrationChinese Clinical Trial Registry (ChiCTR1800015797) Registered on 20/04/2018. https://www.chictr.org.cn.

Soft, Conductive, and Anti‐Freezing Conducting Polymer Organohydrogels

AbstractSoft and conductive materials are highly desirable for wearable electronics. In particular, anti‐freezing, long‐water retention, and highly conductive gels with Young's modulus matching that of biological tissues, show promise in bioelectronics. Herein, soft organohydrogels obtained by mixing poly (3,4‐ethylenedioxythiophene) doped with polystyrene sulfonate (PEDOT:PSS), ethylene glycol (EG), and tannic acid (TA) are reported. The PEDOT/EG/TA organohydrogels exhibit a low compressive Young's modulus of ≈20 kPa, a conductivity of ≈6 S cm−1, as well as anti‐freezing and water retention properties. Epidermal patch electrodes prepared using the PEDOT/EG/TA gel exhibit low skin–electrode impedance at low frequency (1–100 Hz) and high‐quality electrocardiography (ECG) and electromyography signal recordings. Moreover, these gels demonstrate long‐term stability with high ECG recording quality after being placed under ambient conditions for seven days.

Analysis of ANN and Fuzzy Logic Dynamic Modelling to Control the Wrist Exoskeleton

Human intention has long been a primary emphasis in the field of electromyography (EMG) research. This being considered, the movement of the exoskeleton hand can be accurately predicted based on the user's preferences. The EMG is a nonlinear signal formed by muscle contractions as the human hand moves and easily captured noise signal from its surroundings. Due to this fact, this study aims to estimate wrist desired velocity based on EMG signals using ANN and FL mapping methods. The output was derived using EMG signals and wrist position were directly proportional to control wrist desired velocity. Ten male subjects, ranging in age from 21 to 40, supplied EMG signal data set used for estimating the output in single and double muscles experiments. To validate the performance, a physical model of an exoskeleton hand was created using Sim-mechanics program tool. The ANN used Levenberg training method with 1 hidden layer and 10 neurons, while FL used a triangular membership function to represent muscles contraction signals amplitude at different MVC levels for each wrist position. As a result, PID was substituted to compensate fluctuation of mapping outputs, resulting in a smoother signal reading while improving the estimation of wrist desired velocity performance. As a conclusion, ANN compensates for complex nonlinear input to estimate output, but it works best with large data sets. FL allowed designers to design rules based on their knowledge, but the system will struggle due to the large number of inputs. Based on the results achieved, FL was able to show a distinct separation of wrist desired velocity hand movement when compared to ANN for similar testing datasets due to the decision making based on rules setting setup by the designer.

Influence of different calibration methods on surface electromyography-informed musculoskeletal models with few input signals

BackgroundAlthough model personalization is critical when assessing individuals with morphological or neurological abnormalities, or even non-disabled subjects, its translation into routine clinical settings is hampered by the cumbersomeness of experimental data acquisition and lack of resources, which are linked to high costs and long processing pipelines. Quantifying the impact of neglecting subject-specific information in simulations that aim to estimate muscle forces with surface electromyography informed modeling approaches, can address their potential in relevant clinical questions. The present study investigates how different methods to fine-tune subject-specific neuromuscular parameters, reducing the number of electromyography input data, could affect the estimation of the unmeasured excitations and the musculotendon forces. MethodsThree-dimensional motion analysis was performed on 8 non-disabled adult subjects and 13 electromyographic signals captured. Four neuromusculoskeletal models were created for 8 participants: a reference model driven by a large set of sEMG signals; two models informed by four electromyographic signals but calibrated in different fashions; a model based on static optimization. FindingsThe electromyography-informed models better predicted experimental excitations, including the unmeasured ones. The model based on static optimization obtained less reliable predictions of the experimental data. When comparing the different reduced models, no major differences were observed, suggesting that the less complex model may suffice for predicting muscle forces with a small set of input in clinical gait analysis tasks. InterpretationQuantitative model performance evaluation in different conditions provides an objective indication of which method yields the most accurate prediction when a small set of electromyographic recordings is available.

"Avatar", a Modified Ex vivo Work Loop Experiments Using In vivo Strain and Activation.

Movement behaviors are emergent features of dynamic systems that result from muscle force production and work output. The interplay between neural and mechanical systems occurs at all levels of biological organization concurrently, from the tuning of leg muscle properties while running to the dynamics of the limbs interacting with the ground. Understanding the conditions under which animals shift their neural control strategies toward intrinsic muscle mechanics ('preflexes') in the control hierarchy would allow muscle models to predict in vivo muscle force and work more accurately. To understand in vivo muscle mechanics, ex vivo investigation of muscle force and work under dynamically varying strain and loading conditions similar to in vivo locomotion is required. In vivo strain trajectories typically exhibit abrupt changes (i.e., strain and velocity transients) that arise from interactions among neural activation, musculoskeletal kinematics, and loads applied by the environment. The principal goal of our "avatar" technique is to investigate how muscles function during abrupt changes in strain rate and loading when the contribution of intrinsic mechanical properties to muscle force production may be highest. In the "avatar" technique, the traditional work-loop approach is modified using measured in vivo strain trajectories and electromyographic (EMG) signals from animals during dynamic movements to drive ex vivo muscles through multiple stretch-shortening cycles. This approach is similar to the work-loop technique, except that in vivo strain trajectories are scaled appropriately and imposed on ex vivo mouse muscles attached to a servo motor. This technique allows one to: (1) emulate in vivo strain, activation, stride frequency, and work-loop patterns; (2) vary these patterns to match in vivo force responses most accurately; and (3) vary specific features of strain and/or activation in controlled combinations to test mechanistic hypotheses.

Electromyographic activation levels of gluteus maximus, hamstrings and quadriceps in squat and hip thrust exercises: a systematic review

This systematic review aimed to compare the activation levels of hip and knee extensors between the squat exercises and their variations and the hip thrust exercise (HT). To this end, articles published with a time window starting in November 2022 were collected from the electronic databases PubMed, Scopus and Web of Science. The following selection criteria were adopted: cross-sectional or longitudinal study (experimental or cohort); studies that evaluated the neuromuscular activation during the squat and HT exercises or their variations; studies that included healthy, injury-free participants; studies that analyzed the amplitude of electromyographic signals. In total, 4 articles met the study criteria. The following results were found: a) The HT provided greater activation of the hip extensors; b) The squat and its variations increased muscle activation in the vastus lateralis; c) The results are inconclusive for activation of the biceps femoris. The study presented some limitations, such as the low number of articles found, small sample size, lack of studies with a more complete analysis by EMG between the muscle groups of the lower limbs, and lack of chronic records related to muscle hypertrophy and neuromuscular activity.

EEG signatures of low back and knee joint pain during movement execution: a short report.

Chronic musculoskeletal pain has a high prevalence between European citizens, affecting their quality of life and their ability to work. The plastic changes associated with the occurrence of chronic musculoskeletal pain are still not fully understood. The current short report investigated the possible changes in brain activity caused by pain during movement in two of the most common musculoskeletal pain disorders in Denmark, knee pain and low back pain. Electroencephalography (EEG) was recorded from 20 participants (5 participants with knee pain, 5 with low back pain and 10 healthy controls). Participants with pain performed a movement that evoked pain in the area of interest, and the healthy controls performed the same movement. Electromyographic (EMG) signals were also collected to identify movement initiation. No differences were observed in brain activity of participants with pain and healthy controls during rest. During movement execution, though, participants with pain showed significantly higher event related synchronization in the alpha and beta bands compared to healthy controls. These changes could be related to higher cognitive processing, possibly due to the attempt of suppressing the pain. These results highlight the importance of assessing cortical activity during movement to reveal plastic changes due to musculoskeletal pain. This adds to our knowledge regarding plastic changes in cortical activity related to musculoskeletal pain in different locations. Such knowledge could help us identify neurophysiological markers for clinical changes and contribute to the development of new treatment approaches based on neuromodulation such as neurofeedback.

Flexion Angle Estimation from Single Channel Forearm EMG Signals using Effective Features

Electromyography (EMG) records the electrical activity generated by skeletal muscles, offering valuable insights into muscle function and movement. To address the complexity of EMG signals, various signal analysis methods have been developed in the time and frequency domains for engineering applications like myoelectric control of prosthetics and movement analysis. In this study, EMG signals were acquired from ten healthy volunteers in different forearm positions using a Myoware Muscle Sensor and MPU6050 board. From each EMG signal, root mean square (RMS), standard deviation (STD), and mean absolute value (MAV) were computed and selected as representative features. These features were then fed into an LDA classifier to estimate forearm flexion angles. The study aims to compare the effectiveness of features calculated from the EMG signal and those derived from its discrete wavelet decomposition. The experimental results demonstrate the proposed method's efficiency in estimating forearm flexion angles using a single channel of EMG signals, achieving an average classification accuracy of 97.50 % across four gesture classes.

RESEARCH ON GAIT RECOGNITION OF SURFACE EMG SIGNAL BASED ON MPSO-LSTM ALGORITHM

The growing interest in gait recognition based on surface electromyography (sEMG) signals is attributed to their capability to anticipate motion characteristics during human movement. This paper focuses on gait pattern recognition using sEMG signals. Initially, the muscles responsible for collecting sEMG signals are determined based on the distinct characteristics of human gait, and data for 12 different gait patterns are collected. Subsequently, the acquired sEMG signals undergo preprocessing and feature extraction stages. Moreover, various algorithms relevant to gait classification based on surface myoelectric signals are investigated. In this study, we propose an improved particle swarm optimization algorithm (MPSO-LSTM) for accurately classifying gait patterns using surface myoelectric signals. Experimental results demonstrate the effectiveness of the MPSO-LSTM algorithm in gait recognition based on sEMG signals.

Targeted muscle reinnervation: a surgical technique of human-machine interface for intelligent prosthesis

To review targeted muscle reinnervation (TMR) surgery for the construction of intelligent prosthetic human-machine interface, thus providing a new clinical intervention paradigm for the functional reconstruction of residual limbs in amputees. Extensively consulted relevant literature domestically and abroad and systematically expounded the surgical requirements of intelligent prosthetics, TMR operation plan, target population, prognosis, as well as the development and future of TMR. TMR facilitates intuitive control of intelligent prostheses in amputees by reconstructing the "brain-spinal cord-peripheral nerve-skeletal muscle" neurotransmission pathway and increasing the surface electromyographic signals required for pattern recognition. TMR surgery for different purposes is suitable for different target populations. TMR surgery has been certified abroad as a transformative technology for improving prosthetic manipulation, and is expected to become a new clinical paradigm for 2 million amputees in China.

Design and analysis of a passive exoskeleton with its hip joint energy-storage.

A novel passive hip exoskeleton has been designed and built with the aim of reducing metabolic consumption during walking by a passive way of storing the negative mechanical energy in the deceleration phase and releasing it in the acceleration phase. A ratchet spiral spring mechanism with a set of double stable switches is designed inside the exoskeleton for the above purpose. An analysis is conducted on the mechanism and the switching timing for the energy management to automatically store or release the energy according to the biomechanics of walking. In addition, a gravity-balance mechanism embedded inside the exoskeleton is designed as well to minimize the influence of the lower limb weight on muscle work. Human-exoskeleton interaction has been studied using the Opensim software, and simulation results demonstrated the effectiveness of the exoskeleton in reducing metabolic consumption during walking. An exoskeleton prototype has been built and tested with experiments measuring assistive torque and surface electromyography signal, confirming the effectiveness of the gravity-balance mechanism and energy-storage method, as well as the exoskeleton's actual assistive effect.

A Smartphone-Based sEMG Signal Analysis System for Human Action Recognition.

In lower-limb rehabilitation, human action recognition (HAR) technology can be introduced to analyze the surface electromyography (sEMG) signal generated by movements, which can provide an objective and accurate evaluation of the patient's action. To balance the long cycle required for rehabilitation and the inconvenient factors brought by wearing sEMG devices, a portable sEMG signal acquisition device was developed that can be used under daily scenarios. Additionally, a mobile application was developed to meet the demand for real-time monitoring and analysis of sEMG signals. This application can monitor data in real time and has functions such as plotting, filtering, storage, and action capture and recognition. To build the dataset required for the recognition model, six lower-limb motions were developed for rehabilitation (kick, toe off, heel off, toe off and heel up, step back and kick, and full gait). The sEMG segment and action label were combined for training a convolutional neural network (CNN) to achieve high-precision recognition performance for human lower-limb actions (with a maximum accuracy of 97.96% and recognition accuracy for all actions reaching over 97%). The results show that the smartphone-based sEMG analysis system proposed in this paper can provide reliable information for the clinical evaluation of lower-limb rehabilitation.

Fatiguing Joint Angle Does Not Influence Torque and Neuromuscular Responses Following Sustained, Isometric Forearm Flexion Tasks Anchored to Perceptual Intensity in Men.

This study examined the effects of joint angle (JA) on maximal voluntary isometric contraction (MVIC) and neuromuscular responses following fatiguing tasks anchored to RPE. Nine men (mean ± SD: age = 20.7 ± 1.2 yrs) performed forearm flexion MVICs at elbow JAs of 75° and 125° before and after sustained, isometric forearm flexion tasks to failure at fatiguing joint angles (FJA) of 75° and 125° anchored to RPE = 8. The amplitude and frequency of the electromyographic and mechanomyographic signals were recorded. Neuromuscular efficiency was calculated by dividing normalized torque by normalized electromyographic amplitude. A dependent t-test was used to assess the mean difference for time to task failure (TTF) between FJA. Repeated measure ANOVAs were used to assess mean differences for pre-test to post-test MVIC and neuromuscular responses. There was no significant difference between FJA for TTF (p = 0.223). The MVIC (collapsed across FJA and MVIC JA) decreased from pre-test to post-test (51.1 ± 5.0 vs. 45.3 ± 5.6 Nm, p < 0.001). Normalized neuromuscular parameters remained unchanged (p > 0.05). The FJA resulted in similar torque and neuromuscular responses, and the decreases in MVIC were not tracked by changes in the neuromuscular parameters. Thus, the neuromuscular parameters were not sensitive to fatigue, and pre-test to post-test measures may be compared between different FJA.

Diagnosis of patellofemoral osteoarthritis using enhanced sequential deep learning techniques

The surface electromyography (sEMG) signal is a complex interference pattern resulting from the electrical activity of contracting muscles, which directly correlates with muscle activity and exercise level. Patellofemoral osteoarthritis (PF OA) refers to the softening and destruction of the articular cartilage of the knee cap, which is important to diagnose in the early phase. The aim of this study is to develop a predictive model, called the Enhanced Sequencing Deep Learning (PESDL) Algorithm, to detect PF OA from the sEMG signal. The algorithm consists of four main modules: (i) Data Acquisition Module (DAM), (ii) Signal Preprocessing Module (SPM), (iii) Data Augmentation and Concatenation Module (DACM), and (iv) Lower Limb Classification (LLC). The sEMG signals from five core muscles of the lower limb were acquired during stepping stairs up and down, and machine learning was used to obtain the muscle activation data signals. The acquired data was preprocessed, augmented, concatenated, and shuffled. Three feedforward deep networks (RNN, LSTM, and GRU) were used to classify the lower limb sEMG time-sequence data, with the GRU network showing better performance than the other two. The model's performance was evaluated using seven performance measures and 10-fold cross-validation. The maximum values of feedforward deep networks (RNN), LSTM, and GRU for the five muscles (RF, BF, VM, ST, and FX) using seven performance measures were as follows: RNN (0.961, 0.154, 0.943, 0.244, 1.0, 1.0, 0.899), LSTM (0.967, 0.147, 0.945, 0.217, 0.997, 1.0, 0.927), and GRU (0.976, 0.111, 0.949, 0.212, 0.996, 0.998, 0.938), respectively, listed in the order of accuracy, loss, validation accuracy, validation loss, recall, precision, and F1-score. Comparisons with other state-of-the-art models using the same datasets demonstrated the effective performance of the predictive model. The results suggest that RNN models, particularly GRU and LSTM, can be effective for detecting PF OA, and the specific RNN architecture used can have a significant impact on performance.

Changes in normalized mutual information in response to strength training: An ancillary analysis of a quasi-randomized controlled trial.

The aim of the present investigation was twofold. (1) to assess test-retest reliability of normalized mutual information (NMI) values extracted from the surface electromyography (sEMG) signal of muscles pairs of the upper body during dynamic bench press at a high load, and (2) to assess changes in NMI values from before to after a five-week quasi-randomized controlled bench press training intervention. For test-retest reliability, 20 strength trained males (age 25 ± 2 years, height 1.81 ± 0.07 m) performed two three-repetition maximum (3RM) tests in bench press, while sEMG was recorded from six upper body muscles. Tests were separated by 8.2 ± 2.9 days. For the training intervention, 17 male participants (age 26 ± 5 years, height 1.80 ± 0.07 m) trained bench press specific strength training for 5 weeks (TRA), while 13 male participants (age 23 ± 3 years, height 1.80 ± 0.08 m) constituted a control group (CON). 3RM bench press test and sEMG recordings were carried out before and after the intervention period. The NMI values ranged from poor to almost perfect reliability, with the majority displaying substantial reliability. TRA displayed a significant decrease in NMI values during the concentric phase for two agonist-agonist muscle pairs, while one agonist-agonist and two agonist-antagonist muscle pairs increased the NMI values during the eccentric phase. The observed changes did not exceed the minimal detectable threshold, and we therefore cannot surely ascertain that the changes observed in NMI values reflect genuine neural adaptations.

Sex-related differences in motor unit behavior are influenced by myosin heavy chain during high- but not moderate-intensity contractions.

Motor unit recruitment and firing rate patterns of the vastus lateralis (VL) have not been compared between sexes during moderate- and high-intensity contraction intensities. Additionally, the influence of fiber composition on potential sex-related differences remains unquantified. Eleven males and 11 females performed 40% and 70% maximal voluntary contractions (MVCs). Surface electromyographic (EMG) signals recorded from the VL were decomposed. Recruitment thresholds (RTs), MU action potential amplitudes (MUAPAMP ), initial firing rates (IFRs), mean firing rates (MFRs), and normalized EMG amplitude (N-EMGRMS ) at steady torque were analyzed. Y-intercepts and slopes were calculated for MUAPAMP , IFR, and MFR versus RT relationships. Type I myosin heavy chain isoform (MHC) was determined with muscle biopsies. There were no sex-related differences in MU characteristics at 40% MVC. At 70% MVC, males exhibited greater slopes (p = 0.002) for the MUAPAMP , whereas females displayed greater slopes (p = 0.001-0.007) for the IFR and MFR versus RT relationships. N-EMGRMS at 70% MVC was greater for females (p < 0.001). Type I %MHC was greater for females (p = 0.006), and was correlated (p = 0.018-0.031) with the slopes for the MUAPAMP , IFR, and MFR versus RT relationships at 70% MVC (r = -0.599-0.585). Both sexes exhibited an inverse relationship between MU firing rates and recruitment thresholds. However, the sex-related differences in MU recruitment and firing rate patterns and N-EMGRMS at 70% MVC were likely due to greater type I% MHC and smaller twitch forces of the higher threshold MUs for the females. Evidence is provided that muscle fiber composition may explain divergent MU behavior between sexes.

Electromyography Indices of Handgrip Force with Swinging Motion

Electromyography (EMG) is a powerful tool for studying muscle activity, but there is limited research on EMG signals in the muscles of the human forearm, which poses challenges for prosthetic hand development. This study utilized the maximum voluntary contraction (MVC) normalization method to analyze the flexor carpi radialis muscle during handgrip and swinging motions. The MVC indices revealed a significant proportion of high-amplitude MVC results. We conducted three statistical analyses to validate the indices. One-way ANOVA showed significant differences in mean values among the seven subjects during the percent MVC test. RMS study demonstrated a linear correlation between muscle contraction and movement. Boxplot analysis revealed variations within the interquartile range and median values across the entire MVC range. To achieve these results, we employed an eighth-order Gaussian function for curve fitting and exponential weighted moving average. The median interquartile range showed high discrepancies, while the differences between MVC increments were minimal, providing reliable indices for swinging motion. This suggests that the fat layer thickness may influence the muscle signal's frequency characteristics. In summary, our study highlights the untapped potential of EMG signals in the forearm muscles for prosthetic hand development. By employing MVC normalization and conducting rigorous statistical analyses, we uncovered significant findings that contribute to advancements in this field. Our insights provide hope and inspiration for researchers and practitioners seeking to enhance prosthetic technology.