Electromyography-Based Quantitative Representation Method for Upper-Limb Elbow Joint Angle in Sagittal Plane

Pang, Muye; Guo, Shuxiang; Huang, Qiang; Ishihara, Hidenori; Hirata, Hideyuki

doi:10.1007/s40846-015-0033-8

Cited by 42 publications

(25 citation statements)

References 25 publications

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“…Results for the linear model show similar precision values to those achieved by other authors (PANG et al, 2015;LIU;HERZOG;SAVELBERG, 1999;RAHMATIAN;MAHJOOB;HANACHI, 2016;MAMIKOGLU et al, 2016). For most of the simulations, the model achieved correlations above 90% and RMSE under 20 • .…”

Section: Part V Discussion Conclusion and Future Work 8 Discussionsupporting

confidence: 85%

EMG-driven exoskeleton control.

Sommer¹

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The need for mechanisms that assist human movements has been increasing due to the rising number of people that has some kind of movement disability. In this scenario, it is of great importance the development of control methods that assist the interface between a motor assistive device and its user. This work proposes a controller for an exoskeleton with one degree of freedom, using surface electromyography signals from the user as the input signal. An exoskeleton was adapted to serve as platform for the developed control method. To create an EMG-to-Angle model, a set of experiments were carried out with six subjects. The experiment consisted of a series of continuous and discrete elbow flexion and extension movements with different load levels. Using the experimental data, linear (ARIMAX) and non linear (Hammerstein-Wiener) system identification methods were evaluated to determine which is the best candidate for the estimation of the EMG-to-Angle model, based on its accuracy and ease of implementation. A new experiment was conducted to develop a real-time controller, based on FIR model and tested in a real-time application. Tests showed that the controller is capable of estimating the elbow joint angle with correlation above 70% and root-mean-square error below 25 • when compared to the measured elbow joint angles.

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Section: Part V Discussion Conclusion and Future Work 8 Discussionsupporting

confidence: 85%

EMG-driven exoskeleton control.

Sommer¹

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“…Cavallaro et al [5] constructed a sEMG-based HMI by using an optimized Hill's muscle model based on genetic algorithms, which resulted in a high correlation between the predicted results of the model and the measured data. M. Pang et al [6] presented a Hill-type-based muscular model and a state switching model for the continuous estimation of elbow joint angle, and the predicted results were used to control an exoskeleton device. S. Zhang et al [7] proposed a forearm muscle strength estimation method based on musculoskeletal model with Bayesian linear regression algorithm for calibrating parameters.…”

Section: Introductionmentioning

confidence: 99%

Knee Joint Angle Prediction Based on Muscle Synergy Theory and Generalized Regression Neural Network

Liu

et al. 2018

2018 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM)

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Continuous joint motion estimation plays an important part in accomplishing more compliant and safer human-machine interaction (HMI). Surface electromyogram (sEMG) signals, which contain abundant motion information, can be used as a source for continuous joint motion estimation. In this paper, a knee joint angle prediction system based on muscle synergy theory and generalized regression neural network (GRNN) was proposed. The wavelet transform threshold method was used for sEMG signals and angle trajectories denoising. The time-domain features wave-length extracted from four-channel sEMG signals were decomposed into a synergy matrix and an activation coefficient matrix by using nonnegative matrix factorization based on muscle synergy theory. A GRNN based on golden-section search was employed to build the activation model mapping from the activation coefficients to the knee joint angles, so as to realize the continuous knee joint angle estimation. The experimental results show that the average coefficient of determination is 0.933. In addition, a user graphic interface based on the Java platform was designed to display the dynamic sEMG data and predicted knee joint angles in real time.

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“…Lee proposed a method to estimate knee joint angle using least square support vector machine (LS-SVM) [11]. Previous researchers were also used Hill-based muscle model to estimate the elbow-joint angle [13]. Pau used Genetic Algorithm (GA) to optimize the estimate, however, the GA is a high time consuming to perform an optimization [12].…”

Section: Introductionmentioning

confidence: 99%

“…The calibration process is conducted by multiplying the estimated angle (the output of the filtering process) with the maximum value of the range of motion (in this case is 140°). The performance of the proposed and standar method was evaluated using the root mean square error (RMSE) as a standard measurement of the elbow joint angle estimation [10,12,13]. The detail of the comparison between the proposed and standard method is shown in Figure 3.…”

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confidence: 99%

Adaptive threshold to compensate the effect of muscle fatigue on elbow-joint angle estimation based on electromyography

Triwiyanto¹,

Wahyunggoro²,

Nugroho³

et al. 2018

JMES

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Muscle fatigue is a major problem in the study based on electromyography (EMG) signal. When the muscle in the fatigue condition, the power of the EMG signal increases significantly. This phenomenon can be a problem in the elbow-joint angle estimation. The purpose of this study is to develop a method in order to compensate the effect of the muscle fatigue on the elbow-joint angle estimation based on time domain features using EMG signal. The EMG signal was collected from biceps while the subjects performed a fatiguing motion of flexion and extension. The EMG was extracted using four time-domain features, namely zero crossing (ZC), sign slope change (SSC), Wilson amplitude (WAMP) and myopulse percentage rate (MYOP). The yielded features were filtered using second order Butterworth low pass filter. In the proposed method, to compensate the effect of the muscle fatigue, the RMS of the EMG signal was calculated for every cycle and used it as a threshold value of the features. The results show that the proposed method is able to compensate the effect of muscle fatigue with a consistent root mean square error (RMSE). The improvement of the performance ranges between 17.41% and 37.9% (for all adaptive features).

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Electromyography-Based Quantitative Representation Method for Upper-Limb Elbow Joint Angle in Sagittal Plane

Cited by 42 publications

References 25 publications

EMG-driven exoskeleton control.

EMG-driven exoskeleton control.

Knee Joint Angle Prediction Based on Muscle Synergy Theory and Generalized Regression Neural Network

Adaptive threshold to compensate the effect of muscle fatigue on elbow-joint angle estimation based on electromyography

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