Estimation of Upper Limb Joint Angle Using Surface EMG Signal

Aung, Yee Mon; Al-Jumaily, Adel

doi:10.5772/56717

Cited by 56 publications

(26 citation statements)

References 15 publications

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“…Extracting features such as root mean square and the absolute value of amplitude and then establishing a motion model with neural networks constitute an effective method for estimating continuous joint motion [14,15]. e estimation can also be performed through the physiological muscle model, for which the Hill-based muscle model (HMM) is often used [16,17].…”

Section: Introductionmentioning

confidence: 99%

sEMG-MMG State-Space Model for the Continuous Estimation of Multijoint Angle

Chen

Miran

et al. 2020

Complexity

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Continuous joint angle estimation plays an important role in motion intention recognition and rehabilitation training. In this study, a surface electromyography- (sEMG-) mechanomyography (MMG) state-space model is proposed to estimate continuous multijoint movements from sEMG and MMG signals accurately. The model combines forward dynamics with a Hill-based muscle model that estimates joint torque only in a nonfeedback form, making the extended model capable of predicting the multijoint motion directly. The sEMG and MMG features, including the Wilson amplitude and permutation entropy, are then extracted to construct a measurement equation to reduce system error and external disturbances. Using the proposed model, a closed-loop prediction-correction approach, unscented particle filtering, is used to estimate the joint angle from sEMG and MMG signals. Comprehensive experiments are conducted on the human elbow and shoulder joint, and remarkable improvements are demonstrated compared with conventional methods.

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Section: Introductionmentioning

confidence: 99%

sEMG-MMG State-Space Model for the Continuous Estimation of Multijoint Angle

Chen

Miran

et al. 2020

Complexity

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“…One of control signals required to operate a power-assisted robotic system can be obtained by estimating the joint angle based on the obtained sEMG [7]. Several studies propose mathematical models to estimate joint angles [8], [9].…”

Section: Introductionmentioning

confidence: 99%

Estimation of joint angle based on surface electromyogram signals recorded at different load levels

Azab

Arvanch

Mihaylova

2017

2017 39th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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Abstract-To control upper-limb exoskeletons and prostheses, surface electromyogram (sEMG) is widely used for estimation of joint angles. However, the variations in the load carried by the user can substantially change the recorded sEMG and consequently degrade the accuracy of joint angle estimation. In this paper, we aim to deal with this problem by training classification models using a pool of sEMG data recorded from all different loads. The classification models are trained as either subject-specific or subject-independent, and their results are compared with the performance of classification models that have information about the carried load. To evaluate the proposed system, the sEMG signals are recorded during elbow flexion and extension from three participants at four different loads (i.e. 1, 2, 4 and 6 Kg) and six different angles (i.e. 0, 30 , 60, 90, 120, 150 degrees). The results show while the loads were assumed unknown and the applied training data was relatively small, the proposed joint angle estimation model performed significantly above the chance level in both the subject-specific and subject-independent models. However, transferring from known to unknown load in the subject-specific classifiers leads to 20% to 32% loss in the average accuracy.

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“…However, only a few DOFs were analyzed (i.e., 1 or 2), since the nonlinearity of the model equations and a large number of the unknown parameters for each muscle made the analysis rather difficult [7]. Compared with physiological models, extracting continuous movement variables of joints from sEMG signals by using regression models, such as neural network, support vector machine, is another more feasible and convenient approach [1,11]. In [11], a back propagation neural network was developed to estimate the shoulder and elbow joint angles from sEMG signals, and the estimated results were used to control a virtual human model to simulate human's movements.…”

Section: Introductionmentioning

confidence: 99%

A hybrid EMG model for the estimation of multijoint movement in activities of daily living

Ding

Zhao

Han

2014

2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI)

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Accurately identifying human's intent of motion from electromyography (EMG) signals is the key to implement EMG-based HRI (Human-Robot Interface) systems. Human's intent of motion includes motion modes and continuous movement variables. In this paper, a hybrid EMG-to-motion model is constructed by combining a classification model and a regression model. Based on a proper division for joints, the classification model is utilized to recognize the motion modes of 'small' joints; meanwhile, the regression model is utilized to estimate the continuous movement variables of 'big' joints. Furthermore, a Bayesian network (BN) model, which sufficiently employs context information of a task, is also involved into the hybrid model to improve its performances for motion estimation. Experiments have been conducted with three subjects to demonstrate the feasibility of the proposed methods. In these experiments, the motion modes of hand and wrist, and the continuous elbow angles are estimated with sEMG signals considering a 'drinking' task. Finally, an upper limb prosthetic is controlled to simulate human's movement in a 'drinking' task.

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Estimation of Upper Limb Joint Angle Using Surface EMG Signal

Cited by 56 publications

References 15 publications

sEMG-MMG State-Space Model for the Continuous Estimation of Multijoint Angle

sEMG-MMG State-Space Model for the Continuous Estimation of Multijoint Angle

Estimation of joint angle based on surface electromyogram signals recorded at different load levels

A hybrid EMG model for the estimation of multijoint movement in activities of daily living

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