Development of a sEMG-Based Joint Torque Estimation Strategy Using Hill-Type Muscle Model and Neural Network

Dawen, Xu; Wu, Qingcong; Zhu, Yanghui

doi:10.1007/s40846-020-00539-2

Cited by 17 publications

(13 citation statements)

References 31 publications

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“…As no multi-day studies into knee joint angle or torque prediction based on EMG were performed to our knowledge, we cannot compare the results of the other days to related work. Cimolato et al (2020) found an average NRMSE of 24.0% with their Hybrid model and Xu et al (2020) found an average RMSE of 5.8% with their Hybrid approach. The application of Xu et al was on the upper limb which is different than our application in the lower limb.…”

Section: Discussionmentioning

confidence: 96%

“…These EMG signals were then used as input for a calibrated NMS model to predict joint torque, which resulted in an average normalized root mean squared error (NRMSE) of 24.0 ± 11.0%. Xu et al (2020) developed an EMG-based elbow joint torque estimation strategy using a Hill-Type Muscle Model and a neural network. The neural network was used to estimate muscle activation which was used as input for the Hill-Type model.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Day EMG-Based Knee Joint Torque Estimation Using Hybrid Neuromusculoskeletal Modelling and Convolutional Neural Networks

et al. 2022

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Proportional control using surface electromyography (EMG) enables more intuitive control of a transfemoral prosthesis. However, EMG is a noisy signal which can vary over time, giving rise to the question what approach for knee torque estimation is most suitable for multi-day control. In this study we compared three different modelling frameworks to estimate knee torque in non-weight-bearing situations. The first model contained a convolutional neural network (CNN) which mapped EMG to knee torque directly. The second used a neuromusculoskeletal model (NMS) which used EMG, muscle tendon unit lengths and moment arms to compute knee torque. The third model (Hybrid) used a CNN to map EMG to specific muscle activation, which was used together with NMS components to compute knee torque. Multi-day measurements were conducted on ten able-bodied participants who performed non-weight bearing activities. CNN had the best performance in general and on each day (Normalized Root Mean Squared Error (NRMSE) 9.2 ± 4.4%). The Hybrid model (NRMSE 12.4 ± 3.4%) was able to outperform NMS (NRMSE 14.3 ± 4.2%). The NMS model showed no significant difference between measurement days. The CNN model and Hybrid models had significant performance differences between the first day and all other days. CNNs are suited for multi-day torque estimation in terms of error rate, outperforming the other two model types. NMS was the only model type which was robust over all days. This study investigated the behavior of three model types over multiple days, giving insight in the most suited modelling approach for multi-day torque estimation to be used in prosthetic control.

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Section: Discussionmentioning

confidence: 96%

Section: Introductionmentioning

confidence: 99%

Multi-Day EMG-Based Knee Joint Torque Estimation Using Hybrid Neuromusculoskeletal Modelling and Convolutional Neural Networks

et al. 2022

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“…Using the musculoskeletal model to predict the assist force and map it to the control command of the exoskeleton is a more closely coupled and natural method of human-robot interaction [26]. AAN control based on musculoskeletal model needs to collect a large number of motion parameters and human physiological parameters to build a complex musculoskeletal model of upper limb, and the process of muscle strength estimation is complex [27,28]. In order to meet the needs of actual control, it is necessary to simplify and optimize the multi degree of freedom upper limb musculoskeletal model reasonably.…”

Section: Introductionmentioning

confidence: 99%

Design and Assist-as-Needed Control of Flexible Elbow Exoskeleton Actuated by Nonlinear Series Elastic Cable Driven Mechanism

Zhang

et al. 2021

Actuators

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Exoskeletons can assist the daily life activities of the elderly with weakened muscle strength, but traditional rigid exoskeletons bring parasitic torque to the human joints and easily disturbs the natural movement of the wearer’s upper limbs. Flexible exoskeletons have more natural human-machine interaction, lower weight and cost, and have great application potential. Applying assist force according to the patient’s needs can give full play to the wearer’s remaining muscle strength, which is more conducive to muscle strength training and motor function recovery. In this paper, a design scheme of an elbow exoskeleton driven by flexible antagonistic cable actuators is proposed. The cable actuator is driven by a nonlinear series elastic mechanism, in which the elastic elements simulate the passive elastic properties of human skeletal muscle. Based on an improved elbow musculoskeletal model, the assist torque of exoskeleton is predicted. An assist-as-needed (AAN) control algorithm is proposed for the exoskeleton and experiments are carried out. The experimental results on the experimental platform show that the root mean square error between the predicted assist torque and the actual assist torque is 0.00226 Nm. The wearing experimental results also show that the AAN control method designed in this paper can reduce the activation of biceps brachii effectively when the exoskeleton assist level increases.

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“…Especially in rehabilitation training, the estimation of joint torque can not only provide a basis for judging the degree of rehabilitation of patients but also help rehabilitation equipment to identify the movement intention of operators more accurately. Some researchers use the surface electromyogram (sEMG) signal of biceps brachii to estimate the exercise intensity of subjects and map it to the elbow torque and design the control strategy of the rehabilitation robot based on torque estimation [ 1 – 3 ]. Applying the estimation results of ankle torque based on sEMG to the sinusoidal trajectory tracking task of the ankle exoskeleton robot can help exoskeleton equipment achieve more natural movement [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

“…Hou et al established the elbow neuromusculoskeletal model to estimate the joint torque during flexion and extension [ 15 ]. Joint torque prediction based on musculoskeletal model needs to collect a large number of motion parameters and human physiological parameters, and the process of muscle strength estimation is complex [ 1 ]. To meet the practical needs, it is necessary to simplify and optimize the musculoskeletal model.…”

Section: Introductionmentioning

confidence: 99%

An Improved EMG-Driven Neuromusculoskeletal Model for Elbow Joint Muscle Torque Estimation

Tao

et al. 2021

Applied Bionics and Biomechanics

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The accurate measurement of human joint torque is one of the research hotspots in the field of biomechanics. However, due to the complexity of human structure and muscle coordination in the process of movement, it is difficult to measure the torque of human joints in vivo directly. Based on the traditional elbow double-muscle musculoskeletal model, an improved elbow neuromusculoskeletal model is proposed to predict elbow muscle torque in this paper. The number of muscles in the improved model is more complete, and the geometric model is more in line with the physiological structure of the elbow. The simulation results show that the prediction results of the model are more accurate than those of the traditional double-muscle model. Compared with the elbow muscle torque simulated by OpenSim software, the Pearson correlation coefficient of the two shows a very strong correlation. One-way analysis of variance (ANOVA) showed no significant difference, indicating that the improved elbow neuromusculoskeletal model established in this paper can well predict elbow muscle torque.

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Development of a sEMG-Based Joint Torque Estimation Strategy Using Hill-Type Muscle Model and Neural Network

Cited by 17 publications

References 31 publications

Multi-Day EMG-Based Knee Joint Torque Estimation Using Hybrid Neuromusculoskeletal Modelling and Convolutional Neural Networks

Multi-Day EMG-Based Knee Joint Torque Estimation Using Hybrid Neuromusculoskeletal Modelling and Convolutional Neural Networks

Design and Assist-as-Needed Control of Flexible Elbow Exoskeleton Actuated by Nonlinear Series Elastic Cable Driven Mechanism

An Improved EMG-Driven Neuromusculoskeletal Model for Elbow Joint Muscle Torque Estimation

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