Prediction of Knee Joint Moment by Surface Electromyography of the Antagonistic and Agonistic Muscle Pairs

Li, Yurong; Zhang, Qianhui; Zeng, Nianyin; Du, Min

doi:10.1109/access.2019.2923747

Cited by 9 publications

(6 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…sEMG is the sum of the action potentials generated by the movement of muscle motor units, and its signal generation is 20-200 ms ahead of an action [9]. Since sEMG directly reflects the movement state of each muscle and can reflect the movement intentions of the human body [10], it can better establish the humanmachine cooperative closed-loop control system of rehabilitation robots. Compared with inertial sensors, sEMG has more potential to realize man-machine collaboration.…”

Section: Introductionmentioning

confidence: 99%

A Continuous Estimation Model of Upper Limb Joint Angles by Using Surface Electromyography and Deep Learning Method

Chen

et al. 2019

IEEE Access

View full text Add to dashboard Cite

The continuous control of rehabilitation robots based on surface electromyography (sEMG) is a natural control strategy that can ensure human safety and ease the discomfort of human-machine coupling. However, current models for estimating movement of the upper limb focus on two dimensions movement, and models of three dimensions movement are too complex. In this paper, a simple-structure temporal information-based model for upper limb motion was proposed. An experiment of the multijoint motion of the upper limb was carried out. We studied the touching motion and the compound task motion of the upper limbs. The touch motor task consists of three tasks, namely, shoulder abduction, shoulder forward bend and finger-nose. The compound tasks include driving and fetching objects. Three-dimensional upper limb movement data and sEMG signals of seven upper limb muscles were recorded from seven healthy subjects. Model training was carried out after data preprocessing and feature extraction. Then, 120 s of upper limb motion data was used to verify the performance of the model proposed in this paper. The estimated accuracy of the model for the touch tasks was 0.9171, and it was 0.8109 for compound tasks. Compared to the multilayer perception (MLP) model, a 13.57% reduction in the root-mean-square error (RSME) was observed. The results show that the model has good accuracy for estimating the angular motion of the upper limb and that it has the potential to be applied for three-dimensional motion control in an upper limb mirror-image therapy rehabilitation robot.

show abstract

Section: Introductionmentioning

confidence: 99%

A Continuous Estimation Model of Upper Limb Joint Angles by Using Surface Electromyography and Deep Learning Method

Chen

et al. 2019

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Since the neural network has a strong nonlinear fitting ability, it is widely used in modeling and optimization of complex nonlinear systems [12], [22], [28]. However, the sEMG signal of the lower limb has complex nonlinearity, strong coupling, and dynamic time-variation, resulting in the lack of stability of the neural network model.…”

Section: B Algorithm Designmentioning

confidence: 99%

“…In the process of the sEMG feature extraction, many researchers used the time or frequency analysis methods to extract feature vectors from sEMG signals [9][10][11][12][13]. For example, sEMG amplitude, root mean square (RMS), zerocrossing (ZC), autoregressive-coefficient, mean absolute value (MAV), fourier transform coefficient, cepstrumcoefficients, peak frequency, and median frequency analysis methods [14,15].…”

Section: Introductionmentioning

confidence: 99%

Feature Extraction and Classification of Lower Limb Motion Based on sEMG Signals

et al. 2020

View full text Add to dashboard Cite

Surface electromyography (sEMG) signals can reflect the body motion information and are widely used in military, medical rehabilitation, industrial production. The lower limb motion classification mainly includes feature extraction and classification model establishment. Firstly, we proposed a feature extraction method based on the wavelet packet transform (WPT) and principal component analysis (PCA). We used the wavelet packet method to decompose the sEMG signals of three muscles in the lower limb and got the 24-dimensional eigenvector. To reduce the calculation and improve the speed of the classification model, we used the PCA method to reduce the dimension of the feature vector and got the 3-dimensional eigenvector. Then, we proposed a method based on the scale unscented Kalman filter (SUKF) and neural network (NN) for lower limb motion classification. Through the scale correction unscented transform (SCUT) could optimize the neural network weight and improve lower limb motion classification accuracy. Finally, the experimental results showed that the average accuracy was 93.7%. Compared with the backpropagation neural network (BPNN) and wavelet neural network (WNN), this method could improve the accuracy and reliability of the lower limb motion classification. INDEX TERMS sEMG signals, lower limb motion classification, feature extraction, wavelet packet transform, principal component analysis, unscented Kalman filter, neural networks.

show abstract

“…Results showed that the wavelet approach could predict joint moments with higher accuracy, however, the method required a high number of inputs (two ground reaction forces and eight EMG signals). Different studies reported that merging kinematics with EMG inputs can lead to better estimation accuracy in comparison to solely using EMG signals, [24]- [27].…”

Section: Introductionmentioning

confidence: 99%

Multi-Joint Leg Moment Estimation During Walking Using Thigh or Shank Angles

Eslamy

Rastgaar

2023

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

To reinstate human-like locomotion by using robotic prosthetics, orthotics or exoskeletons, a main challenge is how to coordinate the motion of these devices with that of the biological limbs. One approach to overcome this challenge is to identify firstly the relationships that exist between the kinematics and kinetics of the lower extremity joints and limbs. In this work we aimed to continuously estimate sagittal plane ankle, knee and hip moments using shank or thigh angles. For this purpose, neural network and wavelets were used in a nonlinear auto-regressive model with exogenous inputs. This approach circumvented the need for switching rules or intermediate parameters. To assess the performance of the estimator, four case studies were developed. First, thigh angles (inputs) were used to estimate hip moments (outputs). Second, thigh angles were used to estimate knee moments. Third, ankle moments were estimated using thigh angles, and in the fourth case study, ankle moments were estimated using shank angles. Three different databases involving 106 subjects at different walking speeds were used to evaluate estimation quality. The testing procedure involved both inter-subject and intra-subject evaluations. The best estimation performance was observed when ankle moments were estimated from shank angles. The weakest estimation performance was observed when knee moments were estimated using thigh angles at 0.5 m/s. For this case, the estimation quality was much better at 1.5 m/s. Average RMS errors were 0.13 -0.15, 0.10 -0.13, and 0.09 -0.12 [Nm/kg] for hip, knee and ankle moments, respectively. Average mean absolute errors MAEs were 0.10 -0.11, 0.07 -0.10, and 0.06 -0.08 [Nm/kg] for hip, knee and ankle moments, respectively. Average correlation coefficients were 0.90 -0.98 and 0.98 -0.99 for hip and ankle moment estimations. The value for knee was comparable only at high speed (0.96 for 1.5 m/s), while it was less accurate at slow speed (0.71 for 0.5 m/s). In general, for all of the joints, the estimation accuracy was comparable with that of other studies, although one source of input was employed (either shank or thigh angle).

show abstract

Prediction of Knee Joint Moment by Surface Electromyography of the Antagonistic and Agonistic Muscle Pairs

Cited by 9 publications

References 33 publications

A Continuous Estimation Model of Upper Limb Joint Angles by Using Surface Electromyography and Deep Learning Method

A Continuous Estimation Model of Upper Limb Joint Angles by Using Surface Electromyography and Deep Learning Method

Feature Extraction and Classification of Lower Limb Motion Based on sEMG Signals

Multi-Joint Leg Moment Estimation During Walking Using Thigh or Shank Angles

Contact Info

Product

Resources

About