Motion intention prediction of upper limb in stroke survivors using sEMG signal and attention mechanism

Li, Juncheng; Tao, Liang; Zeng, Ziniu; Xu, Pengpeng; Chen, Yan; Guo, Zhaoqi; Liang, Zhenhong; Xie, Longhan

doi:10.1016/j.bspc.2022.103981

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…In the pattern recognition process, a feature extraction stage is used to increase the information density of the EMG signals [5] and classifiers are used to identify the preset motion categories. Typically, characteristics in the time and frequency domain are extracted [6], [7], [8]. The proportional control is more applicable when controlling individual degrees of freedom in a continuous manner is needed.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Dexterous Finger Forces With Forearm Rotation Using Motoneuron Discharges

Zheng,

Li,

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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Motor unit (MU) discharge information obtained via electromyogram (EMG) decomposition can be used to decode dexterous multi-finger movement intention for neural-machine interfaces (NMI). However, the variation of the motor unit action potential (MUAP) shape resulted from forearm rotation leads to the decreased performance of EMG decomposition, especially under the real-time condition and then the degradation of motion decoding accuracy. The object of this study was to develop a method to realize the accurate extraction of MU discharge information across forearm pronated/supinated positions in the real-time condition for dexterous multi-finger force prediction. The FastICA-based EMG decomposition technique was used and the proposed method obtained multiple separation vectors for each MU at different forearm positions in the initialization phase. Under the real-time condition, the MU discharge information was extracted adaptively using the separation vector extracted at the nearest forearm position. As comparison, the previous method that utilized a single constant separation vector to extract MU discharges across forearm positions and the conventional method that utilized the EMG amplitude information were also performed. The results showed that the proposed method obtained a significantly better performance compared with the other two methods, manifested in a larger coefficient of determination (R 2 ) and a smaller root mean squared error (RMSE) between the predicted and recorded force. Our results demonstrated the feasibility and the effectiveness of the proposed method to extract MU discharge information during forearm rotation for dexterous force prediction under the real-time conditions. Further development of the proposed method could potentially promote the application of the EMG decomposition technique for continuous dexterous motion decoding in a realistic NMI application scenario.

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Section: Introductionmentioning

confidence: 99%

Prediction of Dexterous Finger Forces With Forearm Rotation Using Motoneuron Discharges

Zheng,

Li,

et al. 2024

IEEE Trans. Neural Syst. Rehabil. Eng.

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“…In contrast with biomechanical signals, physiological signals contain real-time and abundant motion features related to muscle contraction, such as joint angle [10][11][12], joint torque [13,14], muscle contraction force [15] and so on. The physiological signals-based motion intention can be directly decoded via the physiological motion feature; therefore, it can be considered as the SAMI derived from physiological signals, which is different from motion intention perceived via the pHRI force.…”

Section: Introductionmentioning

confidence: 99%

An artificial systems, computational experiments and parallel execution‐based surface electromyogram‐driven anti‐disturbance zeroing neurodynamic strategy for upper limb human‐robot interaction control

Liu

Wang

et al. 2023

CAAI Trans on Intel Tech

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In recent years, intelligent robots are extensively applied in the field of the industry and intelligent rehabilitation, wherein the human‐robot interaction (HRI) control strategy is a momentous part that needs to be ameliorated. Specially, the efficacy and robustness of the HRI control algorithm in the presence of unknown external disturbances deserve to be addressed. To deal with these urgent issues, in this study, artificial systems, computational experiments and a parallel execution intelligent control framework are constructed for the HRI control. The upper limb‐robotic exoskeleton system is re‐modelled as an artificial system. Depending on surface electromyogram‐based subject's active motion intention in the practical system, a non‐convex function activated anti‐disturbance zeroing neurodynamic (NC‐ADZND) controller is devised in the artificial system for parallel interaction and HRI control with the practical system. Furthermore, the linear activation function‐based zeroing neurodynamic (LAF‐ZND) controller and proportional‐derivative (posterior deltoid (PD)) controller are presented and compared. Theoretical results substantiate the global convergence and robustness of the proposed controller in the presence of different external disturbances. In addition, the simulation results verify that the NC‐ADZND controller is better than the LAF‐ZND and the PD controllers in respect of convergence order and anti‐disturbance characteristics.

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Attention-Based Deep Recurrent Neural Network to Estimate Knee Angle During Walking from Lower-Limb EMG

Abdelhady,

Damiano,

Bulea

2023

2023 International Conference on Rehabilitation Robotics (ICORR)

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Motion intention prediction of upper limb in stroke survivors using sEMG signal and attention mechanism

Cited by 5 publications

References 30 publications

Prediction of Dexterous Finger Forces With Forearm Rotation Using Motoneuron Discharges

Prediction of Dexterous Finger Forces With Forearm Rotation Using Motoneuron Discharges

An artificial systems, computational experiments and parallel execution‐based surface electromyogram‐driven anti‐disturbance zeroing neurodynamic strategy for upper limb human‐robot interaction control

Attention-Based Deep Recurrent Neural Network to Estimate Knee Angle During Walking from Lower-Limb EMG

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