A Pilot Study of Individual Muscle Force Prediction during Elbow Flexion and Extension in the Neurorehabilitation Field

Hou, Jiateng; Sun, Yingfei; Sun, Linfu; Pan, Bingyu; Huang, Zhipei; Wu, Jiankang; Zhang, Zhiqiang

doi:10.3390/s16122018

Cited by 17 publications

(12 citation statements)

References 32 publications

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“…During the simulation process based on the model, static optimization unit takes the experimental joint torque and the estimated joint torque obtained by the simulation as input, and uses the optimization algorithm to adjust the parameters in the model within a reasonable preset range, so that the simulated joint torque is more consistent with the experimental joint torque. The objective functions and constraints are as follows [14]:…”

Section: E Static Optimizationmentioning

confidence: 99%

“…T e e e t o subject (6) where is the experimental joint moment, ˆis the estimated joint moment, j e is the experimental muscle excitation for muscle i , j ê is the adjusted muscle excitation for muscle i , Th is the threshold constrained in the interval (0, 1). We set h T = 0.8 in our experiment to acquire an accurate joint moment estimation [14].…”

Section: E Static Optimizationmentioning

confidence: 99%

See 1 more Smart Citation

Upper Limb Muscle Force Estimation During Table Tennis Strokes

Guo

Sun

Ren

et al. 2019

2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

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Based on an EMG-adjusted method in neuromusculoskeletal model, this study aims to predict the individual muscle force in shoulder and elbow during table tennis strokes. Muscle force estimation makes muscle activation analysis more physiological in sports. Twenty subjects, divided into professional group and amateur group, were adopted in this study. They were asked to do a basic stoke motion: backhand block. Surface electromyography (sEMG) of nine muscles was recorded, as well as the motion data collected by three inertial sensors. A Hill-type musculotendon model was then adopted to estimate individual muscle force by combining adjusted sEMG and motion data. The result shows that the method can estimate individual muscle force during table tennis strokes accurately, and the two groups show significant difference in muscle force of shoulders and elbows.

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Section: E Static Optimizationmentioning

confidence: 99%

Section: E Static Optimizationmentioning

confidence: 99%

Upper Limb Muscle Force Estimation During Table Tennis Strokes

Guo

Sun

Ren

et al. 2019

2019 IEEE 16th International Conference on Wearable and Implantable Body Sensor Networks (BSN)

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“…In the real world, common muscular and neurological disorders, such as stroke and hemiplegia, as well as cumulative diseases such as arthritis, lead to movement problems in the skeletal muscle system and severely affect the body's ability to move freely [2]. Rehabilitation with assistive devices has received increasing attention for helping these patients regain their ability to live independently and improve their standard of living.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Knee Extension Force Using Mechanomyography Signals Based on GRA and ICS-SVR

Gao

et al. 2022

Sensors

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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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“…Joint torques calculated based on optical motion capture devices are considered to be the most accurate, but it is impossible to use optical capture devices in practical application. Therefore, some studies estimated joint torque through the musculoskeletal model ( Hou et al, 2016 ; Chen et al, 2017 ). However, the musculoskeletal model contains many physiological parameters that are difficult to measure.…”

Section: Introductionmentioning

confidence: 99%

A Co-driven Functional Electrical Stimulation Control Strategy by Dynamic Surface Electromyography and Joint Angle

Zhao

Wang

et al. 2022

Front. Neurosci.

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Functional electrical stimulation (FES) is widely used in neurorehabilitation to improve patients’ motion ability. It has been verified to promote neural remodeling and relearning, during which FES has to produce an accurate movement to obtain a good efficacy. Therefore, many studies have focused on the relationship between FES parameters and the generated movements. However, most of the relationships have been established in static contractions, which leads to an unsatisfactory result when applied to dynamic conditions. Therefore, this study proposed a FES control strategy based on the surface electromyography (sEMG) and kinematic information during dynamic contractions. The pulse width (PW) of FES was determined by a direct transfer function (DTF) with sEMG features and joint angles as the input. The DTF was established by combing the polynomial transfer functions of sEMG and joint torque and the polynomial transfer functions of joint torque and FES. Moreover, the PW of two FES channels was set based on the muscle synergy ratio obtained through sEMG. A total of six healthy right-handed subjects were recruited in this experiment to verify the validity of the strategy. The PW of FES applied to the left arm was evaluated based on the sEMG of the right extensor carpi radialis (ECR) and the right wrist angle. The coefficient of determination (R2) and the normalized root mean square error (NRMSE) of FES-included and voluntary wrist angles and torques were used to verify the performance of the strategy. The result showed that this study achieved a high accuracy (R2 = 0.965 and NRMSE = 0.047) of joint angle and a good accuracy (R2 = 0.701 and NRMSE = 0.241) of joint torque reproduction during dynamic movements. Moreover, the DTF in real-time FES system also had a nice performance of joint angle fitting (R2 = 0.940 and NRMSE = 0.071) and joint torque fitting (R2 = 0.607 and NRMSE = 0.303). It is concluded that the proposed strategy is able to generate proper FES parameters based on sEMG and kinematic information for dynamic movement reproduction and can be used in a real-time FES system combined with bilateral movements for better rehabilitation.

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A Pilot Study of Individual Muscle Force Prediction during Elbow Flexion and Extension in the Neurorehabilitation Field

Cited by 17 publications

References 32 publications

Upper Limb Muscle Force Estimation During Table Tennis Strokes

Upper Limb Muscle Force Estimation During Table Tennis Strokes

Estimation of Knee Extension Force Using Mechanomyography Signals Based on GRA and ICS-SVR

A Co-driven Functional Electrical Stimulation Control Strategy by Dynamic Surface Electromyography and Joint Angle

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