Journal articles on the topic 'SEMG-force model'
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Hou, Wensheng, Xiaolin Zheng, Yingtao Jiang, Jun Zheng, Chenglin Peng, and Rong Xu. "A STUDY OF MODELS FOR HANDGRIP FORCE PREDICTION FROM SURFACE ELECTROMYOGRAPHY OF EXTENSOR MUSCLE." Biomedical Engineering: Applications, Basis and Communications 21, no. 02 (April 2009): 81–88. http://dx.doi.org/10.4015/s1016237209001131.
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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).
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GAO, YONGSHENG, SHENGXIN WANG, FEIYUN XIAO, and JIE ZHAO. "AN ANGLE-EMG BIOMECHANICAL MODEL OF THE HUMAN ELBOW JOINT." Journal of Mechanics in Medicine and Biology 16, no. 06 (September 2016): 1650078. http://dx.doi.org/10.1142/s0219519416500780.
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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.
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Wang, Yuan, Fan Li, Haoting Liu, Zhiqiang Zhang, Duming Wang, Shanguang Chen, Chunhui Wang, and Jinhui Lan. "Robust muscle force prediction using NMFSEMD denoising and FOS identification." PLOS ONE 17, no. 8 (August 3, 2022): e0272118. http://dx.doi.org/10.1371/journal.pone.0272118.
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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%.
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Khoshdel, Vahab, and Alireza Akbarzadeh. "An optimized artificial neural network for human-force estimation: consequences for rehabilitation robotics." Industrial Robot: An International Journal 45, no. 3 (May 21, 2018): 416–23. http://dx.doi.org/10.1108/ir-10-2017-0190.
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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.
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Lv, Ying, Qingli Zheng, Xiubin Chen, Yi Jia, Chunsheng Hou, and Meiwen An. "Analysis on Muscle Forces of Extrinsic Finger Flexors and Extensors in Flexor Movements with sEMG and Ultrasound." Mathematical Problems in Engineering 2022 (May 12, 2022): 1–10. http://dx.doi.org/10.1155/2022/7894935.
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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.
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YANG, D. D., W. S. HOU, X. Y. WU, J. ZHENG, X. L. ZHENG, Y. T. JIANG, and L. MA. "IMPACT OF FINGERTIP ACTIONS ON TOTAL POWER OF SURFACE ELECTROMYOGRAPHY FROM EXTRINSIC HAND MUSCLES." Journal of Mechanics in Medicine and Biology 12, no. 03 (June 2012): 1250056. http://dx.doi.org/10.1142/s0219519411004800.
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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.
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Wang, Kai, Xianmin Zhang, Jun Ota, and Yanjiang Huang. "Development of an SEMG-Handgrip Force Model Based on Cross Model Selection." IEEE Sensors Journal 19, no. 5 (March 1, 2019): 1829–38. http://dx.doi.org/10.1109/jsen.2018.2883660.
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Hou, Wensheng, Xiaoying Wu, Jun Zheng, Li Ma, Xiaolin Zheng, Yingtao Jiang, Dandan Yang, Shizhi Qian, and Chenglin Peng. "CHARACTERIZATION OF FINGER ISOMETRIC FORCE PRODUCTION WITH MAXIMUM POWER OF SURFACE ELECTROMYOGRAPHY." Biomedical Engineering: Applications, Basis and Communications 21, no. 03 (June 2009): 193–99. http://dx.doi.org/10.4015/s1016237209001258.
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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.
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Marklin, Richard W., Jonathon E. Slightam, Mark L. Nagurka, Casey D. Garces, Lovely Krishen, and Eric H. Bauman. "New Pistol Grip Control for an Electric Utility Aerial Bucket Reduces Risk of Forearm Muscle Fatigue." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (September 2018): 888–92. http://dx.doi.org/10.1177/1541931218621204.
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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.
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Wang, Jinfeng, Muye Pang, Peixuan Yu, Biwei Tang, Kui Xiang, and Zhaojie Ju. "Effect of Muscle Fatigue on Surface Electromyography-Based Hand Grasp Force Estimation." Applied Bionics and Biomechanics 2021 (February 15, 2021): 1–12. http://dx.doi.org/10.1155/2021/8817480.
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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.
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CHEN, JIANGCHENG, XIAODONG ZHANG, LINXIA GU, and CARL NELSON. "ESTIMATING MUSCLE FORCES AND KNEE JOINT TORQUE USING SURFACE ELECTROMYOGRAPHY: A MUSCULOSKELETAL BIOMECHANICAL MODEL." Journal of Mechanics in Medicine and Biology 17, no. 04 (March 2, 2017): 1750069. http://dx.doi.org/10.1142/s0219519417500695.
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Surface electromyography (sEMG) is a useful tool for revealing the underlying musculoskeletal dynamic properties in the human body movement. In this paper, a musculoskeletal biomechanical model which relates the sEMG and knee joint torque is proposed. First, the dynamic model relating sEMG to skeletal muscle activation considering frequency and amplitude is built. Second, a muscle contraction model based on sliding-filament theory is developed to reflect the physiological structure and micro mechanical properties of the muscle. The muscle force and displacement vectors are determined and the transformation from muscle force to knee joint moment is realized, and finally a genetic algorithm-based calibration method for the Newton–Euler dynamics and overall musculoskeletal biomechanical model is put forward. Following the model calibration, the flexion/extension (FE) knee joint torque of eight subjects under different walking speeds was predicted. Results show that the forward biomechanical model can capture the general shape and timing of the joint torque, with normalized mean residual error (NMRE) of [Formula: see text]10.01%, normalized root mean square error (NRMSE) of [Formula: see text]12.39% and cross-correlation coefficient of [Formula: see text]0.926. The musculoskeletal biomechanical model proposed and validated in this work could facilitate the study of neural control and how muscle forces generate and contribute to the knee joint torque during human movement.
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Dorgham, Osama, Ibrahim Al-Mherat, Jawdat Al-Shaer, Sulieman Bani-Ahmad, and Stephen Laycock. "Smart System for Prediction of Accurate Surface Electromyography Signals Using an Artificial Neural Network." Future Internet 11, no. 1 (January 21, 2019): 25. http://dx.doi.org/10.3390/fi11010025.
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Bioelectric signals are used to measure electrical potential, but there are different types of signals. The electromyography (EMG) is a type of bioelectric signal used to monitor and recode the electrical activity of the muscles. The current work aims to model and reproduce surface EMG (SEMG) signals using an artificial neural network. Such research can aid studies into life enhancement for those suffering from damage or disease affecting their nervous system. The SEMG signal is collected from the surface above the bicep muscle through dynamic (concentric and eccentric) contraction with various loads. In this paper, we use time domain features to analyze the relationship between the amplitude of SEMG signals and the load. We extract some features (e.g., mean absolute value, root mean square, variance and standard deviation) from the collected SEMG signals to estimate the bicep’ muscle force for the various loads. Further, we use the R-squared value to depict the correlation between the SEMG amplitude and the muscle loads by linear fitting. The best performance the ANN model with 60 hidden neurons for three loads used (3 kg, 5 kg and 7 kg) has given a mean square error of 1.145, 1.3659 and 1.4238, respectively. The R-squared observed are 0.9993, 0.99999 and 0.99999 for predicting (reproduction step) of smooth SEMG signals.
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FOO, Chee-Sheng, Takahiro KIKUCHI, Yukihiro MICHIWAKI, Takuji KOIKE, and Takuya HASHIMOTO. "Muscle Force Estimation during Swallowing based on Musculoskeletal Model and sEMG Measurement." Proceedings of JSME annual Conference on Robotics and Mechatronics (Robomec) 2017 (2017): 2P2—J03. http://dx.doi.org/10.1299/jsmermd.2017.2p2-j03.
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Hasanzadeh Fereydooni, Rohollah, Hassan Siahkali, Heidar Ali Shayanfar, and Amir Houshang Mazinan. "sEMG-based variable impedance control of lower-limb rehabilitation robot using wavelet neural network and model reference adaptive control." Industrial Robot: the international journal of robotics research and application 47, no. 3 (January 16, 2020): 349–58. http://dx.doi.org/10.1108/ir-10-2019-0210.
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Purpose This paper aims to propose an innovative adaptive control method for lower-limb rehabilitation robots. Design/methodology/approach Despite carrying out various studies on the subject of rehabilitation robots, the flexibility and stability of the closed-loop control system is still a challenging problem. In the proposed method, surface electromyography (sEMG) and human force-based dual closed-loop control strategy is designed to adaptively control the rehabilitation robots. A motion analysis of human lower limbs is performed by using a wavelet neural network (WNN) to obtain the desired trajectory of patients. In the outer loop, the reference trajectory of the robot is modified by a variable impedance controller (VIC) on the basis of the sEMG and human force. Thenceforward, in the inner loop, a model reference adaptive controller with parameter updating laws based on the Lyapunov stability theory forces the rehabilitation robot to track the reference trajectory. Findings The experiment results confirm that the trajectory tracking error is efficiently decreased by the VIC and adaptively correct the reference trajectory synchronizing with the patients’ motion intention; the model reference controller is able to outstandingly force the rehabilitation robot to track the reference trajectory. The method proposed in this paper can better the functioning of the rehabilitation robot system and is expandable to other applications of the rehabilitation field. Originality/value The proposed approach is interesting for the design of an intelligent control of rehabilitation robots. The main contributions of this paper are: using a WNN to obtain the desired trajectory of patients based on sEMG signal, modifying the reference trajectory by the VIC and using model reference control to force rehabilitation robot to track the reference trajectory.
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Ali, W., and S. Kolyubin. "EMG-Based Grasping Force Estimation for Robot Skill Transfer Learning." Nelineinaya Dinamika 18, no. 5 (2022): 0. http://dx.doi.org/10.20537/nd221221.
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In this study, we discuss a new machine learning architecture, the multilayer preceptron-random forest regressors pipeline (MLP-RF model), which stacks two ML regressors of different kinds to estimate the generated gripping forces from recorded surface electromyographic activity signals (EMG) during a gripping task. We evaluate our proposed approach on a publicly available dataset, putEMG-Force, which represents a sEMG-Force data profile. The sEMG signals were then filtered and preprocessed to get the features-target data frame that will be used to train the proposed ML model. The proposed ML model is a pipeline of stacking 2 different natural ML models; a random forest regressor model (RF regressor) and a multiple layer perceptron artificial neural network (MLP regressor). The models were stacked together, and the outputs were penalized by a Ridge regressor to get the best estimation of both models. The model was evaluated by different metrics; mean squared error and coefficient of determination, or $r^{2}$ score, to improve the model prediction performance. We tuned the most significant hyperparameters of each of the MLP-RF model components using a random search algorithm followed by a grid search algorithm. Finally, we evaluated our MLP-RF model performance on the data by training a recurrent neural network consisting of 2 LSTM layers, 2 dropouts, and one dense layer on the same data (as it is the common approach for problems with sequential datasets) and comparing the prediction results with our proposed model. The results show that the MLP-RF outperforms the RNN model.
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Madden, Kaci E., Dragan Djurdjanovic, and Ashish D. Deshpande. "Using a System-Based Monitoring Paradigm to Assess Fatigue during Submaximal Static Exercise of the Elbow Extensor Muscles." Sensors 21, no. 4 (February 3, 2021): 1024. http://dx.doi.org/10.3390/s21041024.
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Current methods for evaluating fatigue separately assess intramuscular changes in individual muscles from corresponding alterations in movement output. The purpose of this study is to investigate if a system-based monitoring paradigm, which quantifies how the dynamic relationship between the activity from multiple muscles and force changes over time, produces a viable metric for assessing fatigue. Improvements made to the paradigm to facilitate online fatigue assessment are also discussed. Eight participants performed a static elbow extension task until exhaustion, while surface electromyography (sEMG) and force data were recorded. A dynamic time-series model mapped instantaneous features extracted from sEMG signals of multiple synergistic muscles to extension force. A metric, called the Freshness Similarity Index (FSI), was calculated using statistical analysis of modeling errors to reveal time-dependent changes in the dynamic model indicative of performance degradation. The FSI revealed strong, significant within-individual associations with two well-accepted measures of fatigue, maximum voluntary contraction (MVC) force (rrm=−0.86) and ratings of perceived exertion (RPE) (rrm=0.87), substantiating the viability of a system-based monitoring paradigm for assessing fatigue. These findings provide the first direct and quantitative link between a system-based performance degradation metric and traditional measures of fatigue.
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Cenit, Mikecon, and Vaibhav Gandhi. "Design and development of the sEMG-based exoskeleton strength enhancer for the legs." Journal of Mechatronics, Electrical Power, and Vehicular Technology 10, no. 2 (November 28, 2019): 61. http://dx.doi.org/10.14203/j.mev.2019.v10.61-71.
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This paper reviews the different exoskeleton designs and presents a working prototype of a surface electromyography (EMG) controlled exoskeleton to enhance the strength of the lower leg. The Computer Aided Design (CAD) model of the exoskeleton is designed, 3D printed with respect to the golden ratio of human anthropometry, and tested structurally. The exoskeleton control system is designed on the LabVIEW National Instrument platform and embedded in myRIO. Surface EMG sensors (sEMG) and flex sensors are used coherently to create different state filters for the EMG, human body posture and control for the mechanical exoskeleton actuation. The myRIO is used to process sEMG signals and send control signals to the exoskeleton. Thus, the complete exoskeleton system consists of sEMG as primary sensor and flex sensor as secondary sensor while the whole control system is designed in LabVIEW. FEA simulation and tests show that the exoskeleton is suitable for an average human weight of 62 kg plus excess force with different reactive spring forces. However, due to the mechanical properties of the exoskeleton actuator, it will require additional lift to provide the rapid reactive impulse force needed to increase biomechanical movement such as squatting up. Finally, with the increasing availability of such assistive devices on the market, the important aspect of ethical, social and legal issues have also emerged and discussed in this paper.
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Cenit, Mikecon, and Vaibhav Gandhi. "Design and development of the sEMG-based exoskeleton strength enhancer for the legs." Journal of Mechatronics, Electrical Power, and Vehicular Technology 11, no. 2 (December 22, 2020): 64. http://dx.doi.org/10.14203/j.mev.2020.v11.64-74.
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This paper reviews the different exoskeleton designs and presents a working prototype of a surface electromyography (EMG) controlled exoskeleton to enhance the strength of the lower leg. The Computer Aided Design (CAD) model of the exoskeleton is designed, 3D printed with respect to the golden ratio of human anthropometry, and tested structurally. The exoskeleton control system is designed on the LabVIEW National Instrument platform and embedded in myRIO. Surface EMG sensors (sEMG) and flex sensors are used coherently to create different state filters for the EMG, human body posture and control for the mechanical exoskeleton actuation. The myRIO is used to process sEMG signals and send control signals to the exoskeleton. Thus, the complete exoskeleton system consists of sEMG as primary sensor and flex sensor as a secondary sensor while the whole control system is designed in LabVIEW. FEA simulation and tests show that the exoskeleton is suitable for an average human weight of 62 kg plus excess force with different reactive spring forces. However, due to the mechanical properties of the exoskeleton actuator, it will require an additional lift to provide the rapid reactive impulse force needed to increase biomechanical movement such as squatting up. Finally, with the increasing availability of such assistive devices on the market, the important aspect of ethical, social and legal issues have also emerged and discussed in this paper.
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Kong, Dezhi, Wendong Wang, Dong Guo, and Yikai Shi. "RBF Sliding Mode Control Method for an Upper Limb Rehabilitation Exoskeleton Based on Intent Recognition." Applied Sciences 12, no. 10 (May 15, 2022): 4993. http://dx.doi.org/10.3390/app12104993.
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Aiming at the lack of active willingness of patients to participate in the current upper limb exoskeleton rehabilitation training control methods, this study proposed a radial basis function (RBF) sliding mode impedance control method based on surface electromyography (sEMG) to identify the movement intention of upper limb rehabilitation. The proposed control method realizes the process of active and passive rehabilitation training according to the wearer’s movement intention. This study first established a joint angle prediction model based on sEMG for the problem of poor human–machine coupling and used the least-squares support vector machine method (LSSVM) to complete the upper limb joint angle prediction. In addition, in view of the problem of poor compliance in the rehabilitation training process, an adaptive sliding mode controller based on the RBF network approximation system model was proposed. In the process of active training, an impedance model was added based on the position loop control, which could dynamically adjust the motion trajectory according to the interaction force. The experiment results showed that the impedance control method based on the RBF could effectively reduce the interaction force between the human and machine to improve the compliance of the exoskeleton manipulator and achieve the purpose of stabilizing the impedance characteristics of the system.
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Ito, A., Y. Tamura, and M. Saito. "Simulation of force in human elbow biceps by a motor system model using SEMG signal." Journal of Biomechanics 39 (January 2006): S494. http://dx.doi.org/10.1016/s0021-9290(06)85020-7.
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Na, Youngjin, and Jung Kim. "Dynamic Elbow Flexion Force Estimation Through a Muscle Twitch Model and sEMG in a Fatigue Condition." IEEE Transactions on Neural Systems and Rehabilitation Engineering 25, no. 9 (September 2017): 1431–39. http://dx.doi.org/10.1109/tnsre.2016.2628373.
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Lu, Wei, Lifu Gao, Huibin Cao, and Zebin Li. "sEMG-Upper Limb Interaction Force Estimation Framework Based on Residual Network and Bidirectional Long Short-Term Memory Network." Applied Sciences 12, no. 17 (August 29, 2022): 8652. http://dx.doi.org/10.3390/app12178652.
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It is of great significance to estimate the interaction force of upper limbs accurately for improving the control performance of human–computer interaction. However, due to the randomness of the input biological signals and the influence of environmental interference, the interaction force is difficult to estimate using the current methods. Therefore, based on the advantages of the Residual Network (ResNet) and Bidirectional Long Short-Term Memory Network (BiLSTM) model, this paper proposes an end-to-end regression model that integrates ResNet and BiLSTM with an attention mechanism. This model is more suitable for time series sEMG signals. Moreover, it improves the feature extraction ability of the signal and improves the accuracy of interaction force estimation. Experimental results show that this method can automatically extract effective features without professional knowledge. In addition, our method is superior to existing methods in estimation accuracy and generalization ability.
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Li, Zebin, Lifu Gao, Wei Lu, Daqing Wang, Huibin Cao, and Gang Zhang. "Estimation of Knee Extension Force Using Mechanomyography Signals Based on GRA and ICS-SVR." Sensors 22, no. 12 (June 20, 2022): 4651. http://dx.doi.org/10.3390/s22124651.
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During lower-extremity rehabilitation training, muscle activity status needs to be monitored in real time to adjust the assisted force appropriately, but it is a challenging task to obtain muscle force noninvasively. Mechanomyography (MMG) signals offer unparalleled advantages over sEMG, reflecting the intention of human movement while being noninvasive. Therefore, in this paper, based on MMG, a combined scheme of gray relational analysis (GRA) and support vector regression optimized by an improved cuckoo search algorithm (ICS-SVR) is proposed to estimate the knee joint extension force. Firstly, the features reflecting muscle activity comprehensively, such as time-domain features, frequency-domain features, time–frequency-domain features, and nonlinear dynamics features, were extracted from MMG signals, and the relational degree was calculated using the GRA method to obtain the correlation features with high relatedness to the knee joint extension force sequence. Then, a combination of correlated features with high relational degree was input into the designed ICS-SVR model for muscle force estimation. The experimental results show that the evaluation indices of the knee joint extension force estimation obtained by the combined scheme of GRA and ICS-SVR were superior to other regression models and could estimate the muscle force with higher estimation accuracy. It is further demonstrated that the proposed scheme can meet the need of muscle force estimation required for rehabilitation devices, powered prostheses, etc.
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Li, Bo, Bo Yuan, Shuai Tang, Yuwen Mao, Dongmei Zhang, Changyun Huang, and Bilian Tan. "Biomechanical design analysis and experiments evaluation of a passive knee-assisting exoskeleton for weight-climbing." Industrial Robot: An International Journal 45, no. 4 (June 18, 2018): 436–45. http://dx.doi.org/10.1108/ir-11-2017-0207.
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Purpose This paper aims to investigate weight-climbing assistance strategy for the biomechanical design of passive knee-assisting exoskeleton (PKAExo) and evaluate a designed PKAExo which stores energy when the knee joint flexes and releases the energy to assist ascending when the knee joint extends. Design/methodology/approach The authors constructed theoretic modeling of human weight-climbing to analyze characteristics of knee angle and moment. They then conducted camera-based movement analysis, muscle strength and endurance tests and surface electromyography (sEMG) measures to verify the relationship of knee angle and moment with both stair height and load weight. Afterwards, the authors proposed an assistant strategy for passive knee assistance, then gave out designed PKAExo and conducted mechanical experiment to test the knee-assisting torque. Finally, the authors conducted comparison experiment based on measuring the sEMG signals of knee extensor to verify the assistance effect of the PKAExo for weight-climbing. Findings The knee extensor produces the maximum force during weight-climbing, and the muscle force provided by knee extensor has significant increasing rate along with the stair height. Thus, the assistance torque of PKAExo is designed to increase nonlinearly along with increasing knee angle. It stores energy when knee flexes and assists when knee extends. Both the mechanical experiment and comparison experiment have demonstrated that the PKAExo is able to provide nonlinear assistance torque for weight-climbing, thus decreasing the average maximum load of knee extensor by about 21 per cent, reducing muscle fatigue and enhancing wearer’s weight-climbing ability. Originality/value The authors construct theoretic maximum force model produced by knee extensor for weight-climbing in static situation and conduct a series of experiments to verify and revise the model, which is the fundamental reference for knee-assisting mechanism designed for weight-climbing. The authors have also provided and validated an assistant strategy and the mechanism based on the biomechanical analysis, which aims to translate wearer’s energy-providing mode form high load to mid-low load by storing energy when knee flexes and assisting when knee extends. The PKAExo decreases the maximum load of knee extensor, reduces muscle fatigue and helps people to easily climb with load.
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Wilcox, M., H. Brown, K. Johnson, M. Sinisi, and T. J. Quick. "An assessment of fatigability following nerve transfer to reinnervate elbow flexor muscles." Bone & Joint Journal 101-B, no. 7 (July 2019): 867–71. http://dx.doi.org/10.1302/0301-620x.101b7.bjj-2019-0005.r1.
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Aims Improvements in the evaluation of outcomes following peripheral nerve injury are needed. Recent studies have identified muscle fatigue as an inevitable consequence of muscle reinnervation. This study aimed to quantify and characterize muscle fatigue within a standardized surgical model of muscle reinnervation. Patients and Methods This retrospective cohort study included 12 patients who underwent Oberlin nerve transfer in an attempt to restore flexion of the elbow following brachial plexus injury. There were ten men and two women with a mean age of 45.5 years (27 to 69). The mean follow-up was 58 months (28 to 100). Repeated and sustained isometric contractions of the elbow flexors were used to assess fatigability of reinnervated muscle. The strength of elbow flexion was measured using a static dynamometer (KgF) and surface electromyography (sEMG). Recordings were used to quantify and characterize fatigability of the reinnervated elbow flexor muscles compared with the uninjured contralateral side. Results The mean peak force of elbow flexion was 7.88 KgF (sd 3.80) compared with 20.65 KgF (sd 6.88) on the contralateral side (p < 0.001). Reinnervated elbow flexor muscles (biceps brachialis) showed sEMG evidence of fatigue earlier than normal controls with sustained (60-second) isometric contraction. Reinnervated elbow flexor muscles also showed a trend towards a faster twitch muscle fibre type. Conclusion The assessment of motor outcomes must involve more than peak force alone. Reinnervated muscle shows a shift towards fast twitch fibres following reinnervation with an earlier onset of fatigue. Our findings suggest that fatigue is a clinically relevant characteristic of reinnervated muscle. Adoption of these metrics into clinical practice and the assessment of outcome could allow a more meaningful comparison to be made between differing forms of treatment and encourage advances in the management of motor recovery following nerve transfer. Cite this article: Bone Joint J 2019;101-B:867–871.
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Adeola-Bello, Zulikha Ayomikun, and Norsinnira Zainul Azlan. "Power Assist Rehabilitation Robot and Motion Intention Estimation." International Journal of Robotics and Control Systems 2, no. 2 (May 14, 2022): 297–316. http://dx.doi.org/10.31763/ijrcs.v2i2.650.
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This article attempts to review papers on power assist rehabilitation robots, human motion intention, control laws, and estimation of power assist rehabilitation robots based on human motion intention in recent years. This paper presents the various ways in which human motion intention in rehabilitation can be estimated. This paper also elaborates on the control laws for the estimation of motion intention of the power assist rehabilitation robot. From the review, it has been found that the motion intention estimation method includes: Artificial Intelligence-based motion intention and Model-based motion intention estimation. The controllers include hybrid force/position control, EMG control, and adaptive control. Furthermore, Artificial Intelligence based motion intention estimation can be subdivided into Electromyography (EMG), Surface Electromyography (SEMG), Extreme Learning Machine (ELM), and Electromyography-based Admittance Control (EAC). Also, Model-based motion intention estimation can be subdivided into Impedance and Admittance control interaction. Having reviewed several papers, EAC and ELM are proposed for efficient motion intention estimation under artificial-based motion intention. In future works, Impedance and Admittance control methods are suggested under model-based motion intention for efficient estimation of motion intention of power assist rehabilitation robot. In addition, hybrid force/position control and adaptive control are suggested for the selection of control laws. The findings of this review paper can be used for developing an efficient power assist rehabilitation robot with motion intention to aid people with lower or upper limb impairment.
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He, Ruihua, Xinyu Sun, Xuedou Yu, Hongtao Xia, and Shuaijie Chen. "Static Model of Athlete’s Upper Limb Posture Rehabilitation Training Indexes." BioMed Research International 2022 (July 18, 2022): 1–9. http://dx.doi.org/10.1155/2022/9353436.
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With the gradual expansion of the development of sports, the level of sports has been rapidly improved. Athletes have to carry out high-intensity and systemic technical movements in training and competition. Some sports have the greatest burden on the shoulder joint. From the observation and investigation of the injured parts of athletes, it is found that the shoulder joint is the most common sports injury, which is the most typical sports injury. Based on the problem of insufficient strength and endurance reserve after rehabilitation of shoulder external rotator injury, it will cause muscle tension and poor extensibility. To prove the improvement effect of functional training and posture index calibration on the poor posture of the shoulder, considering the measurement of global passive torque, this paper uses a limited set of joint angles and corresponding passive torque data in the upper arm lifting trajectory to train the neural network and uses the trained network to predict the passive torque in other upper arm trajectories. The kinematics model of the shoulder joint is established, and the human-computer interaction experiment is designed on the platform of the gesture index manipulator. The passive and active torque components of the shoulder joint in the human-computer interaction process are calculated by measuring the man-machine interaction force of the subjects in the motion state, which is used as the basis for evaluating the active motion intention of the subjects. Surface electromyography (SEMG) was used to calibrate and verify the attitude index of shoulder active torque. The method proposed in this paper is helpful to achieve more efficient on-demand assisted rehabilitation training exercises, which is of great significance to improve the level of rehabilitation training.
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Xu, Lingfeng, Xiang Chen, Shuai Cao, Xu Zhang, and Xun Chen. "Feasibility Study of Advanced Neural Networks Applied to sEMG-Based Force Estimation." Sensors 18, no. 10 (September 25, 2018): 3226. http://dx.doi.org/10.3390/s18103226.
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To find out the feasibility of different neural networks in sEMG-based force estimation, in this paper, three types of networks, namely convolutional neural network (CNN), long short-term memory (LSTM) network and their combination (C-LSTM) were applied to predict muscle force generated in static isometric elbow flexion across three different circumstances (multi-subject, subject-dependent and subject-independent). Eight healthy men were recruited for the experiments, and the results demonstrated that all the three models were applicable for force estimation, and LSTM and C-LSTM achieved better performances. Even under subject-independent situation, they maintained mean RMSE% of as low as 9.07 ± 1.29 and 8.67 ± 1.14. CNN turned out to be a worse choice, yielding a mean RMSE% of 12.13 ± 1.98. To our knowledge, this work was the first to employ CNN, LSTM and C-LSTM in sEMG-based force estimation, and the results not only prove the strength of the proposed networks, but also pointed out a potential way of achieving high accuracy in real-time, subject-independent force estimation.
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Yokoyama, Masayuki, Ryohei Koyama, and Masao Yanagisawa. "An Evaluation of Hand-Force Prediction Using Artificial Neural-Network Regression Models of Surface EMG Signals for Handwear Devices." Journal of Sensors 2017 (2017): 1–12. http://dx.doi.org/10.1155/2017/3980906.
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Hand-force prediction is an important technology for hand-oriented user interface systems. Specifically, surface electromyography (sEMG) is a promising technique for hand-force prediction, which requires a sensor with a small design space and low hardware costs. In this study, we applied several artificial neural-network (ANN) regression models with different numbers of neurons and hidden layers and evaluated handgrip forces by using a dynamometer. A handwear with dry electrodes on the dorsal interosseous muscles was used for our evaluation. Eleven healthy subjects participated in our experiments. sEMG signals with six different levels of forces from 0 N to 200 N and maximum voluntary contraction (MVC) are measured to train and test our ANN regression models. We evaluated three different methods (intrasession, intrasubject, and intersubject evaluation), and our experimental results show a high correlation (0.840, 0.770, and 0.789 each) between the predicted forces and observed forces, which are normalized by the MVC for each subject. Our results also reveal that ANNs with deeper layers of up to four hidden layers show fewer errors in intrasession and intrasubject evaluations.
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Sheahan, Peter J., Joshua G. A. Cashaback, and Steven L. Fischer. "Evaluating the Ergonomic Benefit of a Wrist Brace on Wrist Posture, Muscle Activity, Rotational Stiffness, and Peak Shovel-Ground Impact Force During a Simulated Tree-Planting Task." Human Factors: The Journal of the Human Factors and Ergonomics Society 59, no. 6 (May 9, 2017): 911–24. http://dx.doi.org/10.1177/0018720817708084.
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Background Tree planters are at a high risk for wrist injury due to awkward postures and high wrist loads experienced during each planting cycle, specifically at shovel-ground impact. Wrist joint stiffness provides a measure that integrates postural and loading information. Objective The purpose of this study was to evaluate wrist joint stiffness requirements at the instant of shovel-ground impact during tree planting and determine if a wrist brace could alter muscular contributions to wrist joint stiffness. Method Planters simulated tree planting with and without wearing a brace on their planting arm. Surface electromyography (sEMG) from six forearm muscles and wrist kinematics were collected and used to calculate muscular contributions to joint rotational stiffness about the wrist. Results Wrist joint stiffness increased with brace use, an unanticipated and negative consequence of wearing a brace. As a potential benefit, planters achieved a more neutrally oriented wrist angle about the flexion/extension axis, although a less neutral wrist angle about the ulnar/radial axis was observed. Muscle activity did not change between conditions. Conclusion The joint stiffness analysis, combining kinematic and sEMG information in a biologically relevant manner, revealed clear limitations with the interface between the brace grip and shovel handle that jeopardized the prophylactic benefits of the current brace design. This limitation was not as evident when considering kinematics and sEMG data independently. Application A neuromechanical model (joint rotational stiffness) enhanced our ability to evaluate the brace design relative to kinematic and sEMG parameter-based metrics alone.
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Wang, Mengcheng, Chuan Zhao, Alan Barr, Suihuai Yu, Jay Kapellusch, and Carisa Harris Adamson. "Hand Posture and Force Estimation using Surface Electromyography and an Artificial Neural Network." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 64, no. 1 (December 2020): 1247–48. http://dx.doi.org/10.1177/1071181320641296.
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Prior epidemiological studies have shown that heavy hand exertion force and hand posture (grip versus pinch) are important risk factors for distal upper extremity disorders such as wrist tendinosis and carpal tunnel syndrome (CTS). However, quantifying the magnitude of hand exertions reliably and accurately is challenging and has relied heavily upon subjective worker or analyst observations. Prior studies have used electromyography (EMG) with machine learning models to estimate hand exertion but relatively few studies have assessed whether hand posture and exertion forces can be predicted at varying levels of force exertion, duty cycle and repetition rate. Therefore, the purpose of this study was to develop an approach to estimate hand posture (pinch versus grip) and hand exertion force using forearm surface electromyography (sEMG) and artificial neural networks.
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Low, Kin Huat, Shuxiang Guo, Xinyan Deng, Ravi Vaidyanathan, James Tangorra, Hoon Cheol Park, and Fumiya Iida. "Special Issue on Focused Areas and Future Trends of Bio-Inspired Robots “Analysis, Control, and Design for Bio-Inspired Robotics”." Journal of Robotics and Mechatronics 24, no. 4 (August 20, 2012): 559–60. http://dx.doi.org/10.20965/jrm.2012.p0559.
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The science of biomimetics is about “the abstraction of good design from nature.” The goal of this scientific field is to identify specific desirable features in the biological systems and apply them to the design of new products or systems. Engineers, scientists, entrepreneurs, and business people are increasingly turning towards nature for design inspiration. The combination of biological principles, mechanical engineering, and robotics has opened entirely new areas and possibilities. On the other hand, we can see that nature can serve as an important source of inspiration to foster innovation. Industrial applications designers can exploit millions of years of tinkering and tweaking by borrowing from nature’s best designs and applying these to new problems and situations. Through biomimetics, we are able to learn and mimic the aforementioned abilities from biology to effectively promote the development of science and technology. In this special issue, you will find a total of eleven papers covering various biomimetics research with focus on analysis, control, design, and simulation. The articles in this issue are contributed by authors from several countries (USA, Japan, UK, China, Switzerland, Brunei, and Singapore) and are grouped into three categories: analysis, control, and design. In the first paper, Kim and Kurabayashi formulate the stability conditions for the artificial pheromone potential field. On the basis of the result of the stability analysis, they further presented a pheromone filter for making a smoothing kernel. The proposed filter was applied to the potential field with several peaks and used by the mobile agent. They are developing a fully automated pheromone robotic system, which aims at achieving a system closer to the natural biological world. In the second paper by Zhang and He, the influence of reciprocal effect between swimming models and morphologic on the fin propulsion performance is analyzed. From the simulation and experiments, they find that the compliance of the distribution mode of fin outline with amplitude envelope can generate better propulsion force. The results are useful for the optimal design of undulating robotic fins. For the third paper, Gouwanda and Senanayake introduce the use of wearable wireless gyroscopes for estimating gait stability. An experimental study was conducted to verify the validity of this approach. The result is expected to be employed in clinical research to assist clinicians and biomechanists in further study, which allows clinicians and biomechanists to devise appropriate strategies that improve human walking stability and reduce the risk of falls in the elderly. In another paper, Pang, Guo, and Song present an implementation of a continuous upper limb motion recognition method based on surface electromyography (sEMG) into control of an Upper Limb Exoskeleton Rehabilitation Device (ULERD). Experimental results showed that this method is effective for obtaining a control source through raw sEMG signals derived from the unaffected arm for motor control of a ULERD equipped on the affected arm during bilateral rehabilitation in real-time. There are three papers related to the control of bioinspired robots. In the paper by Sinnet and Ames, a sagittal walking is designed using Human-Inspired Control which produces human-like bipedal walking with good stability properties. The proposed control scheme, which is based on a fundamental understanding of human walking, is validated in both simulation and experiment. In the second paper, Cheng and Deng have presented a filtered-error based controller for attitude stabilization and tracking in flapping flight. By approximating nonlinear terms in the dynamic equation, the controller has successfully achieved stabilization and tracking tasks for two different insect models. Compared to a Linear Quadratic Gaussian (LQG) controller designed solely for stabilization purposes, the current controller achieves faster convergence and a broader stable region. In order to tackle such a discrepancy between biological and artificial systems, Maheshwari, Gunura and Iida present the concept and design of an adaptive clutch mechanism that discretely covers the full-range of dynamics. This novel actuation principle is then tested in a case study of position and trajectory control for a simple pendulum. The preliminary investigation of this actuation principle has shown a few potentially interesting research directions in the future. The four papers in this special issue cover the design and simulation. In the first paper, Chi and Low introduce the background of fin designs for robotic manta ray. After having analyzed and summarized the various designs, the structure of fin ray effect is investigated in depth. Their characteristics in motion are revealed through kinematic analysis, and the potential design for their RoMan IV with such structure is also presented. The work in the second paper by Boxerbaum et al. reports on the design and optimization of a biologically inspired amphibious robot for deployment and operation in an ocean beach environment. The authors present a new design fusing a range of insect-inspired passive mechanisms with active autonomous control architectures to seamlessly adapt to and traverse through a range of challenging substrates both in and out of the water. A bio-inspired adaptive perching mechanism is presented in the third paper by Chi et al. Based on the anatomy analysis of bird’s perching, some guiding principles for the perching mechanism design are obtained. By making use of motion capture system, reliability of the designed perching mechanism under static conditions is validated. Experiment results show that the perching mechanism is applicable to wide ranges of perching angles and target diameters. In the last paper, Guo et al. present virtual-reality simulators for training with force feedback in Minimally Invasive Surgery (MIS). This application allows generating realistic physical-based models of catheters and blood vessels, and enables surgeons to touch, feel and manipulate virtual catheter inside a vascular model through the same surgical operation mode as is used in actual MIS. The special issue of the Journal of Robotics and Mechatronics on Focused Areas and Future Trends of Bio-Inspired Robots at a particularly appropriate time when the area of biomimetics has attracted a growing interest in recent years in developing autonomous robots that can interact in an unknown environment. Research has also shown that biologically inspired robots will exhibit much greater adaptivity and robustness in performance in unstructured environments than today’s conventional robots. This new class of robots will be substantially more compliant and stable than current robots, and will take advantage of new developments in materials, fabrication technologies, sensors and actuators. Applications of bio-inspired robots will include autonomous or semi-autonomous tasks such as reconnaissance and de-mining for small, insect-like robots and human interaction tasks at a larger scale. We would like to thank the authors for contributing their research papers to this special issue, and the reviewers who, in spite of their busy schedules, took time to provide in-depth comments and constructive criticisms. Last but not least, we would like to thank Editor-in-Chief, Professor Tatsuo Arai, for his support and suggestions to our proposal, which makes the publication of this special issue possible. Our heartfelt thanks go to Mr. Kunihiko Uchida of Fuji Technology Press Ltd. for his professional assistance during the editing process of this special section.
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Mokri, Chiako, Mahdi Bamdad, and Vahid Abolghasemi. "Muscle force estimation from lower limb EMG signals using novel optimised machine learning techniques." Medical & Biological Engineering & Computing 60, no. 3 (January 14, 2022): 683–99. http://dx.doi.org/10.1007/s11517-021-02466-z.
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AbstractThe main objective of this work is to establish a framework for processing and evaluating the lower limb electromyography (EMG) signals ready to be fed to a rehabilitation robot. We design and build a knee rehabilitation robot that works with surface EMG (sEMG) signals. In our device, the muscle forces are estimated from sEMG signals using several machine learning techniques, i.e. support vector machine (SVM), support vector regression (SVR) and random forest (RF). In order to improve the estimation accuracy, we devise genetic algorithm (GA) for parameter optimisation and feature extraction within the proposed methods. At the same time, a load cell and a wearable inertial measurement unit (IMU) are mounted on the robot to measure the muscle force and knee joint angle, respectively. Various performance measures have been employed to assess the performance of the proposed system. Our extensive experiments and comparison with related works revealed a high estimation accuracy of 98.67% for lower limb muscles. The main advantage of the proposed techniques is high estimation accuracy leading to improved performance of the therapy while muscle models become especially sensitive to the tendon stiffness and the slack length.
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Potluri, Chandrasekhar, Madhavi Anugolu, Marco P. Schoen, D. Subbaram Naidu, Alex Urfer, and Steve Chiu. "Hybrid fusion of linear, non-linear and spectral models for the dynamic modeling of sEMG and skeletal muscle force: An application to upper extremity amputation." Computers in Biology and Medicine 43, no. 11 (November 2013): 1815–26. http://dx.doi.org/10.1016/j.compbiomed.2013.08.023.
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Lu, Wei, Lifu Gao, Huibin Cao, Zebin Li, and Daqing Wang. "A comparison of contributions of individual muscle and combination muscles to interaction force prediction using KPCA-DRSN model." Frontiers in Bioengineering and Biotechnology 10 (September 7, 2022). http://dx.doi.org/10.3389/fbioe.2022.970859.
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Rapid and accurate prediction of interaction force is an effective way to enhance the compliant control performance. However, whether individual muscles or a combination of muscles is more suitable for interaction force prediction under different contraction tasks is of great importance in the compliant control of the wearable assisted robot. In this article, a novel algorithm that is based on sEMG and KPCA-DRSN is proposed to explore the relationship between interaction force prediction and sEMG signals. Furthermore, the contribution of each muscle to the interaction force is assessed based on the predicted results. First of all, the experimental platform for obtaining the sEMG is described. Then, the raw sEMG signal of different muscles is collected from the upper arm during different contractions. Meanwhile, the output force is collected by the force sensor. The Kernel Principal Component Analysis (KPCA) method is adopted to remove the invalid components of the raw sEMG signal. After that, the processed sequence is fed into the Deep Residual Shrinkage Network (DRSN) to predict the interaction force. Finally, based on the prediction results, the contribution of each sEMG signal from different muscles to the interaction force is evaluated by the mean impact value (MIV) indicator. The experimental results demonstrate that our methods can automatically extract the valid features of sEMG signal and provided fast and efficient prediction. In addition, the single muscle with the largest MIV index could predict the interaction force faster and more accurately than the muscle combination in different contraction tasks. The finding of our research provides a solid evidence base for the compliant control of the wearable robot.
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Shirzadi, Mehdi, Hamid Reza Marateb, Mónica Rojas-Martínez, Marjan Mansourian, Alberto Botter, Fabio Vieira dos Anjos, Taian Martins Vieira, and Miguel Angel Mañanas. "A real-time and convex model for the estimation of muscle force from surface electromyographic signals in the upper and lower limbs." Frontiers in Physiology 14 (February 27, 2023). http://dx.doi.org/10.3389/fphys.2023.1098225.
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Surface electromyography (sEMG) is a signal consisting of different motor unit action potential trains and records from the surface of the muscles. One of the applications of sEMG is the estimation of muscle force. We proposed a new real-time convex and interpretable model for solving the sEMG—force estimation. We validated it on the upper limb during isometric voluntary flexions-extensions at 30%, 50%, and 70% Maximum Voluntary Contraction in five subjects, and lower limbs during standing tasks in thirty-three volunteers, without a history of neuromuscular disorders. Moreover, the performance of the proposed method was statistically compared with that of the state-of-the-art (13 methods, including linear-in-the-parameter models, Artificial Neural Networks and Supported Vector Machines, and non-linear models). The envelope of the sEMG signals was estimated, and the representative envelope of each muscle was used in our analysis. The convex form of an exponential EMG-force model was derived, and each muscle’s coefficient was estimated using the Least Square method. The goodness-of-fit indices, the residual signal analysis (bias and Bland-Altman plot), and the running time analysis were provided. For the entire model, 30% of the data was used for estimation, while the remaining 20% and 50% were used for validation and testing, respectively. The average R-square (%) of the proposed method was 96.77 ± 1.67 [94.38, 98.06] for the test sets of the upper limb and 91.08 ± 6.84 [62.22, 96.62] for the lower-limb dataset (MEAN ± SD [min, max]). The proposed method was not significantly different from the recorded force signal (p-value = 0.610); that was not the case for the other tested models. The proposed method significantly outperformed the other methods (adj. p-value < 0.05). The average running time of each 250 ms signal of the training and testing of the proposed method was 25.7 ± 4.0 [22.3, 40.8] and 11.0 ± 2.9 [4.7, 17.8] in microseconds for the entire dataset. The proposed convex model is thus a promising method for estimating the force from the joints of the upper and lower limbs, with applications in load sharing, robotics, rehabilitation, and prosthesis control for the upper and lower limbs.
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Zhang, Qiang, Natalie Fragnito, Jason R. Franz, and Nitin Sharma. "Fused ultrasound and electromyography-driven neuromuscular model to improve plantarflexion moment prediction across walking speeds." Journal of NeuroEngineering and Rehabilitation 19, no. 1 (August 9, 2022). http://dx.doi.org/10.1186/s12984-022-01061-z.
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Abstract Background Improving the prediction ability of a human-machine interface (HMI) is critical to accomplish a bio-inspired or model-based control strategy for rehabilitation interventions, which are of increased interest to assist limb function post neurological injuries. A fundamental role of the HMI is to accurately predict human intent by mapping signals from a mechanical sensor or surface electromyography (sEMG) sensor. These sensors are limited to measuring the resulting limb force or movement or the neural signal evoking the force. As the intermediate mapping in the HMI also depends on muscle contractility, a motivation exists to include architectural features of the muscle as surrogates of dynamic muscle movement, thus further improving the HMI’s prediction accuracy. Objective The purpose of this study is to investigate a non-invasive sEMG and ultrasound (US) imaging-driven Hill-type neuromuscular model (HNM) for net ankle joint plantarflexion moment prediction. We hypothesize that the fusion of signals from sEMG and US imaging results in a more accurate net plantarflexion moment prediction than sole sEMG or US imaging. Methods Ten young non-disabled participants walked on a treadmill at speeds of 0.50, 0.75, 1.00, 1.25, and 1.50 m/s. The proposed HNM consists of two muscle-tendon units. The muscle activation for each unit was calculated as a weighted summation of the normalized sEMG signal and normalized muscle thickness signal from US imaging. The HNM calibration was performed under both single-speed mode and inter-speed mode, and then the calibrated HNM was validated across all walking speeds. Results On average, the normalized moment prediction root mean square error was reduced by 14.58 % ($$p=0.012$$ p = 0.012 ) and 36.79 % ($$p<0.001$$ p < 0.001 ) with the proposed HNM when compared to sEMG-driven and US imaging-driven HNMs, respectively. Also, the calibrated models with data from the inter-speed mode were more robust than those from single-speed modes for the moment prediction. Conclusions The proposed sEMG-US imaging-driven HNM can significantly improve the net plantarflexion moment prediction accuracy across multiple walking speeds. The findings imply that the proposed HNM can be potentially used in bio-inspired control strategies for rehabilitative devices due to its superior prediction.
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Hua, Shaoyang, Congqing Wang, and Xuewei Wu. "A novel sEMG-based force estimation method using deep-learning algorithm." Complex & Intelligent Systems, April 23, 2021. http://dx.doi.org/10.1007/s40747-021-00338-5.
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AbstractThis paper discusses the problem of force estimation represented by surface electromyography (sEMG) signals collected from an armband-like collection device. The scheme is proposed for the sake of two dimensions of sEMG signals: spatial and temporal information. From the point of space, first, appropriate channel number across all subjects is investigated. During this progress, an electrode channel selection method based on Spearman’s rank order correlation coefficient is utilized to detect signals from active muscle. Then, to reduce the computation and highlight the channel information, linear regression (LR) algorithm is conducted to weight each channel. Besides, the recurrent neural network (RNN) is used to capture the temporal information and model the relation between sEMG and output force. Experiments conducted on four subjects demonstrate that six channels are enough to characterize the muscle activity. By combining the selected channels with different weight coefficients, LR algorithm can fit the output force better than simply averaging them. Furthermore, RNN with long short-term memory cell shows the superiority in time series modeling, which can improve our results to a greater degree. Experimental results prove the feasibility of the proposed method.
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Narayanan, Sidharth, and Venugopal Gopinath. "Generation and analysis of synthetic surface electromyography signals under varied muscle fiber type proportions and validation using recorded signals." Proceedings of the Institution of Mechanical Engineers, Part H: Journal of Engineering in Medicine, January 18, 2023, 095441192211492. http://dx.doi.org/10.1177/09544119221149234.
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The magnitude and duration of muscle force production are influenced by the fiber type proportion. In this work, surface electromyography (sEMG) signals of muscles with varied fiber type proportions, are generated. For this, relevant components of existing models reported in various literature have been adopted. Also, a method to calculate the motor unit size factor is proposed. sEMG signals of adductor pollicis (AP) and triceps brachii (TB) muscles are simulated from the onset of force production to muscle fatigue state at various percentages of maximal voluntary contraction (MVC) values. The model is validated using signals recorded from these muscles using well-defined isometric exercise protocols. Root mean square and mean power spectral density values extracted from the simulated and recorded signals are found to increase for TB and decrease for AP with time. A linear variation of the features with %MVC values is obtained for simulated and experimental results. The Bland-Altman plot is used to analyze the agreement between simulated and experimental feature values. Good agreement is obtained for the feature values at various %MVCs. The mean endurance time calculated using the model is found to be comparable to that of the experimental value. This method can be used to generate sEMG signals of different muscles with varying fiber type ratios under various neuromuscular conditions.
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Chandrapal, Mervin, XiaoQi Chen, WenHui Wang, Benjamin Stanke, and Nicolas Le Pape. "Investigating improvements to neural network based EMG to joint torque estimation." Paladyn, Journal of Behavioral Robotics 2, no. 4 (January 1, 2011). http://dx.doi.org/10.2478/s13230-012-0007-2.
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AbstractAlthough surface electromyography (sEMG) has a high correlation to muscle force, an accurate model that can estimate joint torque from sEMG is still elusive. Artificial neural networks (NN), renowned as universal approximators, have been employed to capture this complex nonlinear relation. This work focuses on investigating possible improvements to the NN methodology and algorithm that would consistently produce reliable sEMG-to-knee-joint torque mapping for any individual. This includes improvements in number of inputs, data normalization techniques, NN architecture and training algorithms. Data (sEMG) from five knee extensor and flexor muscle from one subject were recorded on 10 random days over a period of 3 weeks whilst subject performed both isometric and isokinetic movements. The results indicate that incorporating more muscles into the NN and normalizing the data at each isometric angle prior to NN training improves torque estimation. The mean lowest estimation error achieved for isometric motion was 10.461% (1.792), whereas the lowest estimation errors for isokinetic motion were larger than 20%.
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WAN, LISHUANG, FAHAD ABDULLAH ALQURASHI, and WOO-JIN JUNG. "PRACTICE AND DEVELOPMENT OF SPORTS SOMATIC SCIENCE IN SOCIAL PHYSICAL EDUCATION TEACHING USING FRACTAL THEORY." Fractals 30, no. 02 (February 14, 2022). http://dx.doi.org/10.1142/s0218348x22400886.
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In view of the recognition of human motion function in physical education, a multidimensional evaluation index of motion function based on fatigue, muscle strength and muscle contraction, and relaxation ability is proposed, and the mapping rules among fatigue characteristic value, muscle strength, fractal dimension characteristic change, and motion function state are studied. Based on the characteristics of surface electromyography (sEMG) signal and its generating principle, the collected signal is preprocessed by median filtering to obtain the filtered signal. The effectiveness of the filtering method is proved by comparing the signals before and after filtering. The current muscle strength calculation method based on sEMG signal is analyzed and studied. The muscle strength calculation method of Hill three-element model is used to establish the muscle model through the muscle activity and force–length curve calculated by sEMG signals. The muscle strength is obtained by muscle model, and the feasibility of the experimental method is verified by comparing the calculated actual muscle strength with the theoretical muscle strength. In the experiment, the distribution of different subjects in the multidimensional evaluation index space is studied. Moreover, the mapping relationship between sEMG signal characteristics and human motion function is discussed comprehensively based on theory and data.
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Shi, Lei, Zhen Liua, and Chao Zhang. "A Control Framework of Lower Extremity Rehabilitation Exoskeleton based on Neuro-Muscular-Skeletal Model." Journal of Applied Information Science 3, no. 1 (2015). http://dx.doi.org/10.21863/jais/2015.3.1.002.
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A control system framework of lower extremity rehabilitation exoskeleton robot is presented. It is based on the Neuro-Musculo-Skeletal biological model. Its core composition moudle, the motion intent parser part, mainly comprises of three distinct parts. The first part is signal acquisition of surface electromyography (sEMG) that is the summation of motor unit action potential (MUAP) starting from central nervous system (CNS). sEMG can be used to decode action intent of operator to make the patient actively participate in specific training. As another composition part, a muscle dynamics model that is comprised of activation and contraction dynamic model is developed. It is mainly used to calculate muscle force. The last part is the skeletal dynamic model that is simplified as a linked segment mechanics. Combined with muscle dynamic model, the joint torque exerted by internal muscles can be exported, which can be ued to do a exoskeleton controller design. The developed control framework can make exoskeleton offer assistance to operators during rehabilitation by guiding motions on correct training rehabilitation trajectories, or give force support to be able to perform certain motions. Though the presentation is orientated towards the lower extremity exoskeleton, it is generic and can be applied to almost any part of the human body.
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Mao, He, Peng Fang, Yue Zheng, Lan Tian, Xiangxin Li, Pu Wang, Liang Peng, and Guanglin Li. "Continuous grip force estimation from surface electromyography using generalized regression neural network." Technology and Health Care, September 8, 2022, 1–15. http://dx.doi.org/10.3233/thc-220283.
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BACKGROUND: Grip force estimation is highly required in realizing flexible and accurate prosthetic control. OBJECTIVE: This study presents a method to accurately estimate continuous grip force from surface electromyography (sEMG) under three forearm postures for unilateral amputees. METHODS: Ten able-bodied subjects and a transradial amputee were recruited. sEMG signals were recorded from six forearm muscles on the dominant side of each able-bodied subject and the stump of amputee. Meanwhile, grip force was synchronously measured from the ipsilateral hands of able-bodied subjects and contralateral hand of amputee. Three force profiles (triangle, trapezoid, and fast triangle) were tested under three forearm postures (supination, neutral and pronation). Two algorithms (Generalized Regression Neural Network (GRNN) and Multilinear Regression Model (MLR)) were compared using several EMG features. The estimation performance was evaluated by coefficient of determination (R2) and mean absolute error (MAE). RESULTS: The optimal regressor combining TD and GRNN achieved R2= 96.33 ± 1.13% and MAE= 2.11 ± 0.52% for the intact subjects, and R2= 86.86% and MAE= 2.13% for the amputee. The results indicated that multiple grip force curves under three forearm postures could be accurately estimated for unilateral amputees using mirrored bilateral training. CONCLUSIONS: The proposed method has the potential for precise force control of prosthetic hands.
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Wang, Mengcheng, Chuan Zhao, Alan Barr, Hao Fan, Suihuai Yu, Jay Kapellusch, and Carisa Harris Adamson. "Hand Posture and Force Estimation Using Surface Electromyography and an Artificial Neural Network." Human Factors: The Journal of the Human Factors and Ergonomics Society, May 18, 2021, 001872082110166. http://dx.doi.org/10.1177/00187208211016695.
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Objective The purpose of this study was to develop an approach to predict hand posture (pinch versus grip) and grasp force using forearm surface electromyography (sEMG) and artificial neural networks (ANNs) during tasks that varied repetition rate and duty cycle. Background Prior studies have used electromyography with machine learning models to predict grip force but relatively few studies have assessed whether both hand posture and force can be predicted, particularly at varying levels of duty cycle and repetition rate. Method Fourteen individuals participated in this experiment. sEMG data for five forearm muscles and force output data were collected. Calibration data (25, 50, 75, 100% of maximum voluntary contraction (MVC)) were used to train ANN models to predict hand posture (pinch versus grip) and force magnitude while performing tasks that varied load, repetition rate, and duty cycle. Results Across all participants, overall hand posture prediction accuracy was 79% (0.79 ± .08), whereas overall hand force prediction accuracy was 73% (0.73 ± .09). Accuracy ranged between 0.65 and 0.93 based on varying repetition rate and duty cycle. Conclusion Hand posture and force prediction were possible using sEMG and ANNs, though there were important differences in the accuracy of predictions based on task characteristics including duty cycle and repetition rate. Application The results of this study could be applied to the development of a dosimeter used for distal upper extremity biomechanical exposure measurement, risk assessment, job (re)design, and return to work programs.