Academic literature on the topic 'SEMG-force model'

Force production involves the coordination of multiple muscles, and the produced force levels can be attributed to the electrophysiology activities of those related muscles. This study is designed to explore the activity modes of extensor carpi radialis longus (ECRL) using surface electromyography (sEMG) at the presence of different handgrip force levels. We attempt to compare the performance of both the linear and nonlinear models for estimating handgrip forces. To achieve this goal, a pseudo-random sequence of handgrip tasks with well controlled force ranges is defined for calibration. Eight subjects (all university students, five males, and three females) have been recruited to conduct both calibration and voluntary trials. In each trial, sEMG signals have been acquired and preprocessed with Root–Mean–Square (RMS) method. The preprocessed signals are then normalized with amplitude value of Maximum Voluntary Contraction (MVC)-related sEMG. With the sEMG data from calibration trials, three models, Linear, Power, and Logarithmic, are developed to correlate the handgrip force output with the sEMG activities of ECRL. These three models are subsequently employed to estimate the handgrip force production of voluntary trials. For different models, the Root–Mean–Square–Errors (RMSEs) of the estimated force output for all the voluntary trials are statistically compared in different force ranges. The results show that the three models have different performance in different force ranges. Linear model is suitable for moderate force level (30%–50% MVC), whereas a nonlinear model is more accurate in the weak force level (Power model, 10%–30% MVC) or the strong force level (Logarithmic model, 50%–80% MVC).

The biomechanical model of the human elbow joint is extensively studied. In the model, the surface electromyography (sEMG) is used as the input signal, whereas the muscle force or muscle torque is commonly considered as the output signal. The estimation of the actual muscle force or torque is important to effectively modulate the tremor suppression. However, the measurement of the muscle force or torque in vivo is difficult. In this paper, a new angle-to-EMG biomechanical model of the elbow joint was developed and evaluated by comparing the measured sEMG with the calculated sEMG. Three sources of the sEMG signal, namely, the central nervous system (CNS), the Golgi tendon and the muscle spindle were considered in this model. Furthermore, a local PID algorithm was proposed to describe the impact of the CNS on the motor neuron and the Golgi tendon model was used to transform muscle forces to stimulus signals. The model was calibrated by an improved search procedure combining the Powell search and the direct search to determine optimal model parameters. In the experiment, an sEMG signal acquisition system was established to measure the sEMG signal and the elbow joint angle. The experimental results, the predicted sEMG signal well following the measured sEMG, demonstrated that the calibrated model could be used to estimate in vivo sEMG signals and is beneficial to explore the peripheral neural system and the pathogenesis of tremor.

In this paper, an aliasing noise restraint technique and a system identification-based surface electromyography (sEMG)-force prediction model are proposed to realize a type of robust sEMG and muscle force prediction. For signal denoising, a novel non-negative matrix factorization screening empirical mode decomposition (NMFSEMD) and a fast orthogonal search (FOS)-based muscle force prediction model are developed. First, the NMFSEMD model is used to screen the empirical mode decomposition (EMD) results into the noisy intrinsic mode functions (IMF). Then, the noise matrix is computed using IMF translation and superposition, and the matrix is used as the input of NMF to obtain the denoised IMF. Furthermore, the reconstruction outcome of the NMFSEMD method can be used to estimate the denoised sEMG. Finally, a new sEMG muscle force prediction model, which considers a kind of candidate function in derivative form, is constructed, and a data-training-based linear weighted model is obtained. Extensive experimental results validate the suggested method’s correction: after the NMFSEMD denoising of raw sEMG signal, the signal-noise ratio (SNR) can be improved by about 15.0 dB, and the energy percentage (EP) can be greater than 90.0%. Comparing with the muscle force prediction models using the traditional pretreatment and LSSVM, and the NMFSEMD plus LSSVM-based method, the mean square error (MSE) of our approach can be reduced by at least 1.2%.

Purpose This paper aims to present an application of design of experiments techniques to determine the optimized parameters of artificial neural networks (ANNs), which are used to estimate human force from Electromyogram (sEMG) signals for rehabilitation robotics. Physiotherapists believe, to make a precise therapeutic exercise, we need to design and perform therapeutic exercise base on patient muscle activity. Therefore, sEMG signals are the best tool for using in therapeutic robots because they are related to the muscle activity. Using sEMG signals as input for therapeutic robots need precise human force estimation from sEMG. Furthermore, the ANN estimator performance is highly dependent on the accuracy of the target date and setting parameters. Design/methodology/approach In the previous studies, the force data, which are collected from the force sensors or dynameters, has widely been used as target data in the training phase of learning ANN. However, force sensors or dynameters could measure only contact force. Therefore, the authors consider the contact force, limb’s dynamic and time in target data to increase the accuracy of target data. Findings There are plenty of algorithms that are used to obtain optimal ANN settings. However, to the best of our knowledge, they do not use regression analysis to model the effect of each parameter, as well as present the contribution percentage and significance level of the ANN parameters for force estimation. Originality/value In this paper, a new model to estimate the force from sEMG signals is presented. In this method, the sum of the limb’s dynamics and the contact force is used as target data in the training phase. To determine the limb’s dynamics, the patient’s body and the rehabilitation robot are modeled in OpenSim. Furthermore, in this paper, sEMG experimental data are collected and the ANN parameters based on an orthogonal array design table are regulated to train the ANN. Taguchi is used to find the optimal parameters settings. Next, analysis of variance technique is used to obtain significance level, as well as contribution percentage of each parameter, to optimize ANN’s modeling in human force estimation. The results indicate that the presented model can precisely estimate human force from sEMG signals.

The coupling relationship between surface electromyography (sEMG) signals and muscle forces or joint moments is the basis for sEMG applications in medicine, rehabilitation, and sports. The solution of muscle forces is the key issue. sEMG and Muscle-Tendon Junction (MTJ) displacements of the flexor digitorum superficialis (FDS), flexor digitorum profundus (FDP), and extensor digitorum (ED) were measured during five sets of finger flexion movements. Meanwhile, the muscle forces of FDS, FDP, and ED were calculated by the Finite Element Digital Human Hand Model (FE-DHHM) driven by MTJ displacements. The results showed that, in the initial position of the flexion without resistance, the high-intensity contraction of the ED kept the palm straight and the FDS was involved. The sEMG-force relationship of FDS was linear during the flexion with resistance, while FDP showed a larger sEMG amplitude than FDS, with no obvious linearity with its muscle forces. sEMG-MTJ displacement relationships for FDS and FDP were consistent with the trend of their own sEMG-force relationships. sEMG of ED decreased and then increased during the flexion with resistance, with no obvious linear relationship with muscle forces. The analysis of the proportion of muscle force and integrated EMG (iEMG) reflected the different activation patterns of FDS and ED.

Quantizing the relationship between finger force and multitendoned extrinsic hand muscles could be useful for understanding the control strategies that underlie the coordination of finger movements and forces. The objective of this study is to explore the relationship of fingertip force production and total power of surface electromyography (sEMG) recorded on extrinsic hand muscles under isometric voluntary contraction. Thirteen healthy volunteers were recruited to participate in this study. In the designed force-tracking tasks, all volunteers were required to produce a certain force with either index finger or middle finger to match the target force for 5 s. Meanwhile, the sEMG signals were acquired from two extrinsic hand muscles: extensor digitorum (ED) and flexor digitorum superficialis (FDS). For each trial, sEMG of the effective force segment was extracted; then, the power spectrum was estimated based on autoregressive (AR) model and from which the corresponding total power of sEMG was computed. The experimental results reveal that the total power of sEMG linearly increases with force level regardless of the task finger and extrinsic hand muscle. It is also found that the total power obtained from index finger is significantly less than that of middle finger for FDS at the same force level (p < 0.05), while this kind of statistical significance cannot be found for ED. However, with respect to the measurement of total power, the type of extrinsic hand muscle has not exhibited significantly different contribution to the task finger under a certain fingertip force level. The findings of this study indicate that the total power of the extrinsic hand muscle's sEMG can be used to characterize finger's activities.

Finger's action has been controlled by both intrinsic and extrinsic hand muscles. Characterizing the finger action with the activations of hand muscles could be useful for evaluating the neuromuscular control strategy of finger's motor functions. This study is designed to explore the correlation of isometric fingertip force production and frequency-domain features of surface electromyography (sEMG) recorded on extrinsic hand muscles. To this end, 13 subjects (five male and eight female university students) have been recruited to conduct a target force-tracking task. Each subject is required to produce a certain level of force with either the index or middle fingertip to match the pseudo-random ordered target force level (4N, 6N, or 8N) as accurate as possible. During the finger force production process, the sEMG signals are recorded on two extrinsic hand muscles: flex digitorum superficials (FDS) and extensor digitorum (ED). For each sEMG trail, the power spectrum is estimated with the autoregressive (AR) model and from which the maximum power is obtained. Our experimental results reveal three findings: (1) the maximum power increases with the force level regardless of the force producing finger (i.e. index or middle) and the extrinsic hand muscle (i.e. FDS or ED). (2) The sEMG maximum power of index finger is significantly lower than that of the middle finger under the same force level and extrinsic hand muscle. (3) No significant difference can be found between the maximum powers of FDS and ED. The results indicate that the activations of the extrinsic muscles are affected by both the force level and the force producing finger. Based on our findings, the sEMG maximum power of the extrinsic hand muscles could be used as a key parameter to describe the finger's actions.

Overhead line workers have anecdotally reported elevated levels of fatigue in forearm muscles when operating the pistol grip control that maneuvers an aerial bucket on a utility truck. Previous research with surface electromyographic (sEMG) recordings of forearm muscles corroborated these reports of muscle fatigue. A new pistol grip was designed that reduces the applied force by 50% in all directions of movement. In laboratory testing, sEMG signals were recorded from the upper extremity muscles of twenty subjects, who operated a conventional-force pistol grip and the 50% reduced-force control to move a 1/15 scale model of an aerial truck boom. The muscle that resulted in the greatest sEMG activity (extensor digitorum communis (EDC)) was the muscle that workers typically pointed to when they reported forearm muscle fatigue from using the control. The reduced-forced pistol grip decreased EDC sEMG by an average of 5.6%, compared to the conventional control, increasing the maximum endurance time by 38% according to muscle fatigue models. This study was the first to quantify muscular activity of a new aerial bucket pistol grip control and the results show promise for improving the occupational health of electric utility overhead line workers, specifically reducing muscle fatigue. Before the new design of the pistol grip can be commercialized, it must be tested in the field on actual equipment.

Surface electromyography- (sEMG-) based hand grasp force estimation plays an important role with a promising accuracy in a laboratory environment, yet hardly clinically applicable because of physiological changes and other factors. One of the critical factors is the muscle fatigue concomitant with daily activities which degrades the accuracy and reliability of force estimation from sEMG signals. Conventional qualitative measurements of muscle fatigue contribute to an improved force estimation model with limited progress. This paper proposes an easy-to-implement method to evaluate the muscle fatigue quantitatively and demonstrates that the proposed metrics can have a substantial impact on improving the performance of hand grasp force estimation. Specifically, the reduction in the maximal capacity to generate force is used as the metric of muscle fatigue in combination with a back-propagation neural network (BPNN) is adopted to build a sEMG-hand grasp force estimation model. Experiments are conducted in the three cases: (1) pooling training data from all muscle fatigue states with time-domain feature only, (2) employing frequency domain feature for expression of muscle fatigue information based on case 1, and 3) incorporating the quantitative metric of muscle fatigue value as an additional input for estimation model based on case 1. The results show that the degree of muscle fatigue and task intensity can be easily distinguished, and the additional input of muscle fatigue in BPNN greatly improves the performance of hand grasp force estimation, which is reflected by the 6.3797% increase in R2 (coefficient of determination) value.

Les systèmes neuromusculaire et musculosquelettique sont des systèmes de systèmes complexes qui interagissent parfaitement entre eux afin de produire le mouvement. En y regardant de plus près, ce mouvement est la résultante d'une force musculaire créée à partir d'une activation du muscle par le système nerveux central. En parallèle de cette activité mécanique, le muscle produit aussi une activité électrique elle aussi contrôlée par la même activation. Cette activité électrique peut être mesurée à la surface de la peau à l'aide d'électrode, ce signal enregistré par l'électrode se nomme le signal Électromyogramme de surface (sEMG). Comprendre comment ces résultats de l'activation du muscle sont générés est primordial en biomécanique ou pour des applications cliniques. Évaluer et quantifier ces interactions intervenant durant la contraction musculaire est difficile et complexe à étudier dans des conditions expérimentales. Par conséquent, il est nécessaire de développer un moyen pour pouvoir décrire et estimer ces interactions. Dans la littérature de la bioingénierie, plusieurs modèles de génération de signaux sEMG et de force ont été publiés. Ces modèles sont principalement utilisés pour décrire une partie des résultats de la contraction musculaire. Ces modèles souffrent de plusieurs limites telles que le manque de réalisme physiologique, la personnalisation des paramètres, ou la représentativité lorsqu'un muscle complet est considéré. Dans ce travail de thèse, nous nous proposons de développer un modèle biofidèle, personnalisable et rapide décrivant l'activité électrique et mécanique du muscle en contraction isométrique. Pour se faire, nous proposons d'abord un modèle décrivant l'activité électrique du muscle à la surface de la peau. Cette activité électrique sera commandé par une commande volontaire venant du système nerveux périphérique, qui va activer les fibres musculaires qui vont alors dépolariser leur membrane. Cette dépolarisation sera alors filtrée par le volume conducteur afin d'obtenir l'activité électrique à la surface de la peau. Une fois cette activité obtenue, le système d'enregistrement décrivant une grille d'électrode à haute densité (HD-sEMG) est modélisée à la surface de la peau afin d'obtenir les signaux sEMG à partir d'une intégration surfacique sous le domaine de l'électrode. Dans ce modèle de génération de l'activité électrique, le membre est considéré cylindrique et multi couches avec la considération des tissus musculaire, adipeux et la peau. Par la suite, nous proposons un modèle mécanique du muscle décrit à l'échelle de l'Unité Motrice (UM). L'ensemble des résultats mécaniques de la contraction musculaire (force, raideur et déformation) sont déterminées à partir de la même commande excitatrice du système nerveux périphérique. Ce modèle est basé sur le modèle de coulissement des filaments d'actine-myosine proposé par Huxley que l'on modélise à l'échelle UM en utilisant la théorie des moments utilisée par Zahalak. Ce modèle mécanique est validé avec un profil de force enregistré sur un sujet paraplégique avec un implant de stimulation neurale. Finalement, nous proposons aussi trois applications des modèles proposés afin d'illustrer leurs fiabilités ainsi que leurs utilité. Tout d'abord une analyse de sensibilité globale des paramètres de la grille HDsEMG est présentée. Puis, nous présenterons un travail fait en collaboration avec une autre doctorante une nouvelle étude plus précise sur la modélisation de la relation HDsEMG/force en personnalisant les paramètres afin de mimer au mieux le comportement du Biceps Brachii. Pour conclure, nous proposons un dernier modèle quasi­ dynamique décrivant l'activité électro-mécanique du muscle en contraction isométrique. Ce modèle déformable va actualiser l'anatomie cylindrique du membre sous une hypothèse isovolumique du muscle
The neuromuscular and musculoskeletal systems are complex System of Systems (SoS) that perfectly interact to provide motion. From this interaction, muscular force is generated from the muscle activation commanded by the Central Nervous System (CNS) that pilots joint motion. In parallel an electrical activity of the muscle is generated driven by the same command of the CNS. This electrical activity can be measured at the skin surface using electrodes, namely the surface electromyogram (sEMG). The knowledge of how these muscle out comes are generated is highly important in biomechanical and clinical applications. Evaluating and quantifying the interactions arising during the muscle activation are hard and complex to investigate in experimental conditions. Therefore, it is necessary to develop a way to describe and estimate it. In the bioengineering literature, several models of the sEMG and the force generation are provided. They are principally used to describe subparts of themuscular outcomes. These models suffer from several important limitations such lacks of physiological realism, personalization, and representability when a complete muscle is considered. In this work, we propose to construct bioreliable, personalized and fast models describing electrical and mechanical activities of the muscle during contraction. For this purpose, we first propose a model describing the electrical activity at the skin surface of the muscle where this electrical activity is determined from a voluntary command of the Peripheral Nervous System (PNS), activating the muscle fibers that generate a depolarization of their membrane that is filtered by the limbvolume. Once this electrical activity is computed, the recording system, i.e. the High Density sEMG (HD-sEMG) grid is define over the skin where the sEMG signal is determined as a numerical integration of the electrical activity under the electrode area. In this model, the limb is considered as a multilayered cylinder where muscle, adipose and skin tissues are described. Therefore, we propose a mechanical model described at the Motor Unit (MU) scale. The mechanical outcomes (muscle force, stiffness and deformation) are determined from the same voluntary command of the PNS, and is based on the Huxley sliding filaments model upscale at the MU scale using the distribution-moment theory proposed by Zahalak. This model is validated with force profile recorded from a subject implanted with an electrical stimulation device. Finally, we proposed three applications of the proposed models to illustrate their reliability and usefulness. A global sensitivity analysis of the statistics computed over the sEMG signals according to variation of the HD-sEMG electrode grid is performed. Then, we proposed in collaboration a new HDsEMG/force relationship, using personalized simulated data of the Biceps Brachii from the electrical model and a Twitch based model to estimate a specific force profile corresponding to a specific sEMG sensor network and muscle configuration. To conclude, a deformableelectro-mechanicalmodelcouplingthetwoproposedmodelsisproposed. This deformable model updates the limb cylinder anatomy considering isovolumic assumption and respecting incompressible property of the muscle

Avec le besoin de développer un organe artificiel remplaçant le doigt humain dans le cas d'un déficit et la nécessité de comprendre le fonctionnement de ce système physiologique, un modèle physique inverse du système doigt, permettant de chercher les activations neuronales à partir du mouvement, est nécessaire. Malgré le grand nombre d'études dans la modélisation de la main humaine, presque il n'existe aucun modèle physique inverse du système doigt majeur qui s'intéresse à chercher les activations neuronales. Presque tous les modèles existants se sont intéressés à la recherche des forces et des activations musculaires. L'objectif de la thèse est de présenter un modèle neuromusculo-squelettique du système doigt majeur humain permettant d'obtenir les activations neuronales, les activations musculaires et les forces musculaires des tous les muscles agissants sur le système doigt d'après l'analyse du mouvement. Le but de ce type des modèles est de représenter les caractéristiques essentielles du mouvement avec le plus de réalisme possible. Notre travail consiste à étudier, modéliser et à simuler le mouvement du doigt humain. L'innovation du modèle proposé est le couplage entre la biomécanique et les aspects neurophysiologiques afin de simuler la chaine inverse complet du mouvement en allant des données dynamiques du doigt aux intentions neuronales qui contrôlent les activations musculaires. L'autre innovation est la conception d'un protocole expérimental spécifique qui traite à la fois les données sEMG multicanal et les données cinématiques d'après une procédure de capture de mouvement
With the need to develop an artificial organ replacing the human finger in the case of a deficiency and the need to understand how this physiological system works, an inverse physical model of the finger system for estimating neuronal activations from the movement, is necessary. Despite the large number of studies in the human hand modeling, almost there is no inverse physical model of the middle finger system that focuses on search neuronal activations. Al most all existing models have focused on the research of the muscle forces and muscle activations. The purpose of the manuscript is to present a neuromusculoskeletal model of the human middle finger system for estimating neuronal activations, muscle activations and muscle forces of all the acting muscles after movement analysis. The aim of such models is to represent the essential characteristics of the movement with the best possible realism. Our job is to study, model and simulate the movement of the human finger. The innovation of the proposed model is the coupling between the biomechanical and neurophysiological aspects to simulate the complete inverse movement chain from dynamic finger data to neuronal intents that control muscle activations. Another innovation is the design of a specific experimental protocol that treats both the multichannel sEMG and kinematic data from a data capture procedure of the movement

Modelling of human motion is used in a wide range of applications. An important aspect of accurate representation of human movement is the ability to customize models to account for individual differences. The following work proposes a methodology using Hill-based candidate functions in the Fast Orthogonal Search (FOS) method to predict translational force at the wrist from flexion and extension torque at the elbow. Within this force estimation framework, it is possible to implicitly estimate subject-specific physiological parameters of Hill-based models of upper arm muscles. Surface EMG data from three muscles of the upper arm (biceps brachii, brachioradialis and triceps brachii) were recorded from 10 subjects as they performed isometric contractions at varying elbow joint angles. Estimated muscle activation level and joint kinematic data (joint angle and angular velocity) were utilized as inputs to the FOS model. The resulting wrist force estimations were found to be more accurate for models utilizing Hill-based candidate functions, than models utilizing candidate functions that were not physiologically relevant. Subject-specific estimates of optimal joint angle were determined via frequency analysis of the selected FOS candidate functions. Subject-specific optimal joint angle estimates demonstrated low variability and fell within the range of angles presented in the literature.
Thesis (Master, Electrical & Computer Engineering) -- Queen's University, 2008-10-30 01:32:01.606

Surface Electromyographic (sEMG) signals have been exploited for almost a century, for various clinical and engineering applications. One of the most compelling and altruistic applications being, control of prosthetic devices. The study conducted here looks at the modeling of the force and sEMG signals, using nonlinear Hammerstein-Weiner System Identification techniques. This study involved modeling of sEMG and corresponding force data to establish a relation which can mimic the actual force characteristics for a few particular hand motions. Analysis of the sEMG signals, obtained from specific Motor Unit locations corresponding to the index, middle and ring finger, and the force data led to the following deductions; a) Each motor unit location has to be treated as a separate system, (i.e. extrapolation of models for different fingers cannot be done) b) Fatigue influences the Hammerstein-Wiener model parameters and any control algorithm for implementing the force regimen will have to be adaptive in nature to compensate for the changes in the sEMG signal and c) The results also manifest the importance of the design of the experiments that need to be adopted to comprehensively model sEMG and force.

Traditional electromyopgrahic (EMG) measurements are based on single sensor information. Due to the arrangement of skeletal muscle fibers for hand motions, cross talk is an inherent problem when inferring motion/force potentials from EMG data. This paper studies means of using sensor arrays to infer better motion/force potential for prosthetic hands. In particular, a surface electromyographic (sEMG) sensor array is used to investigate multiple model fusion techniques. This paper provides a comparison between three statistical model selection criteria. The sEMG signals are pre-processed using four filters, Butterworth, Chebyshev type-II, as well as Bayesian filters such as the Exponential and Half-Gaussian filter. Output Error (OE) models were extracted from sEMG data and hand force data and compared using a Bayesian based fusion model. The four different filters effect were quantified based on the OE models performance in matching the actual measured data. The comparison indicates a preference for using the sensor fusion technique with preprocessed EMG data using the Half-Gaussian Bayesian filter and the Kullback Information Criterion (KIC).

The objective of this work is to identify the motor point location from the obtained sEMG signals using Dempster Shafer theory (DST). The proposed technique is applied on data obtained from a male test subject. In particular, the sEMG signals and its corresponding skeletal muscle force signals from the Flexor Digitorum Superficialis are acquired at a sampling rate of 2000 Hz using a Delsys Bangnoli- 16 EMG system. The acquired sEMG signals are rectified and filtered using a Discrete Wavelet Transforms (DWT) with a Daubechies 44 mother wavelet. For the system identification, an Output Error (OE) model structure is assumed to obtain the dynamic relation between the sEMG signal and the corresponding finger force signals. Subsequently, model based probabilities and fuzzy inference based probabilities are obtained for discrete sensor locations of a sEMG sensor array. Considering these evidences, a DST based motor point location identification method is proposed. The results based on one subject show the potential of the proposed theory and approach for affectively identifying motor point locations using an array sEMG sensor.

Background: Unreasonable tasks will increase the person’s physical load, leading to safety accidents and occupational diseases. To ensure a reasonable physical load and improve the operational efficiency of the person as far as possible, it is necessary to predict and evaluate the physical load of workers in real-time.Objective: A prediction model of the physical load intensity of the human body based on a neural network was established, and its effectiveness was verified.Methods: Twelve volunteers completed four movements walking, jogging, climbing, and jumping. The surface electromyography (sEMG) on the left and right sides of the rectus femoris and biceps femoris was measured, and the motor posture of volunteers was obtained by Vicon, the joint torque, maximum muscle activity, and muscular force parameters were calculated based on the reverse dynamic model of human motion. The sEMG eigenvalue and mechanical load parameters in different postures were considered input and output, respectively, and 80% of all data were used as the training set and the rest as the validation set.Results: In this study, we found that the hip joint, knee joint, and ankle joint have a sizeable joint torque during movement, in which the joint torque of the ankle joint is the largest and twice human body weight at its peak. Besides, a larger muscle load occurs at the beginning and end of contact between the human foot and the ground, and the muscle strength of the rectus femoris was significantly higher than that of the biceps femoris (p<0.05). The number of neurons in the input layer, an output layer, and a hidden layer of the model is 32, 13, and 12, respectively. This study found that the prediction error of maximum muscle activity was 6.4%. The average prediction error of joint torque was 8.7%, and the prediction error of the muscular force of the rectus femoris muscle was no more than 9.5%. This model can reasonably predict the physical load of the human body.Conclusions: A workload evaluation model based on the BP neural network was established in this research, which can analyze the biomechanics of the human body in motion and judge the human body’s physical load effectively according to the EMG signal.Application: This model can measure the body load of soldiers and firefighters in real-time during task training and provide a reference for task design.