An EMG-based force prediction and control approach for robot-assisted lower limb rehabilitation

Meng, Wei; Ding, Bo; Zhou, Zude; Liu, Quan; Ai, Qingsong

doi:10.1109/smc.2014.6974250

Cited by 26 publications

(11 citation statements)

References 18 publications

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“…An alternative method is based on the physiological signal regression. Meng et al [150], [151] have proposed an admittance controller that can turn actual interaction force tracking into an EMG-based interactive force prediction. EMG-based interactive torque is predicted using a support vector regression (SVR) algorithm.…”

Section: Physiological Intention Estimation-based Position Assistancementioning

confidence: 99%

A Review on Human–exoskeleton Coordination Towards Lower Limb Robotic Exoskeleton Systems

Ma¹,

Wang²,

Yi³

et al. 2019

International Journal of Robotics and Automation

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The study of lower extremity exoskeleton robots has been pursued for many years and is now receiving significant attention. A number of review articles have focused on describing the mechanical design of exoskeleton robots, their control methods, human-exoskeleton robot interfaces, and so on. None, however, have focused on the coordination of control between humans and these kinds of robots. In this paper, we examine the research regarding human-exoskeleton robot coordinated control to capture the current state-of-the-art. One hundred and fifty six relevant articles were collected and screened from the Web of science, and classified into six categories, based on human neurobiology and exoskeleton robots' assistive effects. These categories were further subdivided according to their perspective on human-exoskeleton robots coordinated control. The paper extensively reviews various control methods we uncovered and how they are coordinated between wearer and exoskeleton robot.

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Section: Physiological Intention Estimation-based Position Assistancementioning

confidence: 99%

A Review on Human–exoskeleton Coordination Towards Lower Limb Robotic Exoskeleton Systems

Ma¹,

Wang²,

Yi³

et al. 2019

International Journal of Robotics and Automation

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“…Many artifacts contaminate the signals of EMG and time-series data is not suitable for demonstration purposes. The traditional methods used the frequency- and time-dependent properties to identify the EMG features under various patterns of knee exercise [ 16 , 17 ]. The existing methods have the limitation of irrelevant feature selection and lower classification performance in muscular paralysis disease prediction.…”

Section: Introductionmentioning

confidence: 99%

Prediction of muscular paralysis disease based on hybrid feature extraction with machine learning technique for COVID-19 and post-COVID-19 patients

Subramani¹,

Srinivas

et al. 2021

Pers Ubiquit Comput

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Many Coronavirus disease 2019 (COVID-19) and post-COVID-19 patients experience muscle fatigues. Early detection of muscle fatigue and muscular paralysis helps in the diagnosis, prediction, and prevention of COVID-19 and post-COVID-19 patients. Nowadays, the biomedical and clinical domains widely used the electromyography (EMG) signal due to its ability to differentiate various neuromuscular diseases. In general, nerves or muscles and the spinal cord influence numerous neuromuscular disorders. The clinical examination plays a major role in early finding and diagnosis of these diseases; this research study focused on the prediction of muscular paralysis using EMG signals. Machine learning–based diagnosis of the diseases has been widely used due to its efficiency and the hybrid feature extraction (FE) methods with deep learning classifier are used for the muscular paralysis disease prediction. The discrete wavelet transform (DWT) method is applied to decompose the EMG signal and reduce feature degradation. The proposed hybrid FE method consists of Yule-Walker, Burg’s method, Renyi entropy, mean absolute value, min-max voltage FE, and other 17 conventional features for prediction of muscular paralysis disease. The hybrid FE method has the advantage of extract the relevant features from the signals and the Relief-F feature selection (FS) method is applied to select the optimal relevant feature for the deep learning classifier. The University of California, Irvine (UCI), EMG-Lower Limb Dataset is used to determine the performance of the proposed classifier. The evaluation shows that the proposed hybrid FE method achieved 88% of precision, while the existing neural network (NN) achieved 65% of precision and the support vector machine (SVM) achieved 35% of precision on whole EMG signal.

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“…Through the analysis and feature extraction of MMG, a recognition model can be built to recognize the movement patterns of limbs, but it is difficult to accurately estimate muscle force. Accurate muscle force estimation has great practical demand and extremely important application value in many fields [16][17][18]. However, there is no mature and stable technology to directly measure muscle force at present.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Knee Joint Extension Force Using Mechanomyography Based on IGWO-SVR Algorithm

Gao

et al. 2021

Electronics

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Muscle force is an important physiological parameter of the human body. Accurate estimation of the muscle force can improve the stability and flexibility of lower limb joint auxiliary equipment. Nevertheless, the existing force estimation methods can neither satisfy the accuracy requirement nor ensure the validity of estimation results. It is a very challenging task that needs to be solved. Among many optimization algorithms, gray wolf optimization (GWO) is widely used to find the optimal parameters of the regression model because of its superior optimization ability. Due to the traditional GWO being prone to fall into local optimum, a new nonlinear convergence factor and a new position update strategy are employed to balance local and global search capability. In this paper, an improved gray wolf optimization (IGWO) algorithm to optimize the support vector regression (SVR) is developed to estimate knee joint extension force accurately and timely. Firstly, mechanomyography (MMG) of the lower limb is measured by acceleration sensors during leg isometric muscle contractions extension training. Secondly, root mean square (RMS), mean absolute value (MAV), zero crossing (ZC), mean power frequency (MPF), and sample entropy (SE) of the MMG are extracted to construct feature sets as candidate data sets for regression analysis. Lastly, the features are fed into IGWO-SVR for further training. Experiments demonstrate that the IGWO-SVR provides the best performance indexes in the estimation of knee joint extension force in terms of RMSE, MAPE, and R compared with the other state-of-art models. These results are expected to become the most effective as guidance for rehabilitation training, muscle disease diagnosis, and health evaluation.

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An EMG-based force prediction and control approach for robot-assisted lower limb rehabilitation

Cited by 26 publications

References 18 publications

A Review on Human–exoskeleton Coordination Towards Lower Limb Robotic Exoskeleton Systems

A Review on Human–exoskeleton Coordination Towards Lower Limb Robotic Exoskeleton Systems

Prediction of muscular paralysis disease based on hybrid feature extraction with machine learning technique for COVID-19 and post-COVID-19 patients

Estimation of Knee Joint Extension Force Using Mechanomyography Based on IGWO-SVR Algorithm

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