A novel method based on long short term memory network and discrete-time zeroing neural algorithm for upper-limb continuous estimation using sEMG signals

Chai, Yuanyuan; Liu, Keping; Li, Chunxu; Sun, Zhongbo; Jin, Long; Shi, Tian

doi:10.1016/j.bspc.2021.102416

Cited by 23 publications

(6 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…b) Deep Learning: Studies [190] and [191] noted that the openloop LSTM had minor prediction errors due to uncertainties in the LSTM modeling process (e.g., number of hidden neurons and dataset sizes) and physiological effects like joint damping. Thus, they combined LSTM with Zeroing Neural Network (L-ZNN) and Noise-Tolerant ZNN (L-NTZNN) to construct error functions using ZNN closed-loop feedback to eliminate errors.…”

Section: ) (Hand) Wrist Elbow and Shoulder Jointsmentioning

confidence: 99%

Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches

Wei,

Zhang,

Xie

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

Upper limb functional impairments persisting after stroke significantly affect patients' quality of life. Precise adjustment of robotic assistance levels based on patients' motion intention using sEMG signals is crucial for active rehabilitation. This paper systematically reviews studies over the past decade on continuous prediction of upper limb single joint and multi-joint combinations motion intention using Model-Based (MB) and Model-Free (MF) approaches, based on 186 relevant studies screened from six major electronic databases. The findings indicate ongoing challenges in terms of subject composition, algorithm robustness and generalization, and feasibility for practical applications. Moreover, it suggests integrating the strengths of both MB and MF approaches to improve existing algorithms. Therefore, future research should further explore personalized MB-MF combination methods incorporating deep learning, attention mechanisms, muscle synergy features, motor unit features, and closed-loop feedback to achieve precise, real-time, and long-duration prediction of multi-joint complex movements, while further refining the transfer learning strategy for rapid algorithm deployment across days and subjects. In summary, this review summarizes the current state and challenges of research, aiming to provide inspiration for future research on predicting upper limb motion intention based on sEMG.

show abstract

Section: ) (Hand) Wrist Elbow and Shoulder Jointsmentioning

confidence: 99%

Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches

Wei,

Zhang,

Xie

2024

IEEE Trans. Neural Syst. Rehabil. Eng.

View full text Add to dashboard Cite

show abstract

“…The sEMG signal can reflect the activity state of the muscle during exercise to a certain extent (Wang et al, 2019 ; Chai et al, 2021 ; Wei et al, 2021 ). Through the corresponding time and frequency domain analysis, the time and frequency domain characteristics and the corresponding muscle characteristics and movement correlation can be obtained, and the muscle function state of the human body during exercise can be obtained.…”

Section: Active Movement Intention Recognition For Upper Limbsmentioning

confidence: 99%

A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals

et al. 2022

Self Cite

View full text Add to dashboard Cite

The use of upper limb rehabilitation robots to assist the affected limbs for active rehabilitation training is an inevitable trend in the field of rehabilitation medicine. In particular, the active motion intention-based control of the upper limb rehabilitation robots to assist subjects in rehabilitation training is a hot research topic in human-computer interaction control. Therefore, improving the accuracy of active motion intention recognition is the premise of the human-machine interaction controller design. Furthermore, there are external disturbances (bounded/unbounded disturbances) during rehabilitation training, which seriously threaten the safety of subjects. Thereby, eliminating external disturbances (especially unbounded disturbances) is the difficulty and key to the human-machine interaction control of the upper limb rehabilitation robots. In response to these problems, based on the surface electromyogram signal of the human upper limb, this paper proposes a fuzzy neural network active motion intention recognition method to explore the internal connection between the surface electromyogram signal of the human upper limb and active motion intention, and improve the real-time and accuracy of recognition. Based on this, two types of human-machine interaction controllers, which can be called as zeroing neural network controller and noise-suppressing zeroing neural network controller are designed to establish a safe and comfortable training environment to avoid secondary damage to the affected limb. Numerical experiments verify the feasibility and effectiveness of the proposed theories and methods.

show abstract

“…Accordingly, it is necessary to apply deep learning models with high accuracy and robustness [ 19 , 20 ]. Deep learning networks, including deep belief networks [ 21 ], long short-term memory (LSTM) [ 22 , 23 , 24 , 25 ], recurrent neural networks (RNN) [ 26 ], and convolutional neural networks (CNN) [ 27 ], are used to perform the self-learning and hierarchical feature representation of sEMG features, thereby improving the accuracy of estimated results. Gautam [ 23 ] proposed a long-term recurrent convolutional network (LRCN) based on transfer learning to estimate the knee joint angle, using CNN to extract features directly from the raw sEMG signal and LSTM for sequence prediction.…”

Section: Introductionmentioning

confidence: 99%

Continuous Motion Estimation of Knee Joint Based on a Parameter Self-Updating Mechanism Model

Li,

Zhang

et al. 2023

Bioengineering

View full text Add to dashboard Cite

Estimation of continuous motion of human joints using surface electromyography (sEMG) signals has a critical part to play in intelligent rehabilitation. Traditional methods always use sEMG signals as inputs to build regression or biomechanical models to estimate continuous joint motion variables. However, it is challenging to accurately estimate continuous joint motion in new subjects due to the non-stationarity and individual differences in sEMG signals, which greatly limits the generalisability of the method. In this paper, a continuous motion estimation model for the human knee joint with a parameter self-updating mechanism based on the fusion of particle swarm optimization (PSO) and deep belief network (DBN) is proposed. According to the original sEMG signals of different subjects, the method adaptively optimized the parameters of the DBN model and completed the optimal reconstruction of signal feature structure in high-dimensional space to achieve the optimal estimation of continuous joint motion. Extensive experiments were conducted on knee joint motions. The results suggested that the average root mean square errors (RMSEs) of the proposed method were 9.42° and 7.36°, respectively, which was better than the results obtained by common neural networks. This finding lays a foundation for the human–robot interaction (HRI) of the exoskeleton robots based on the sEMG signals.

show abstract

A novel method based on long short term memory network and discrete-time zeroing neural algorithm for upper-limb continuous estimation using sEMG signals

Cited by 23 publications

References 33 publications

Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches

Continuous Motion Intention Prediction Using sEMG for Upper-Limb Rehabilitation: A Systematic Review of Model-Based and Model-Free Approaches

A noise-suppressing neural network approach for upper limb human-machine interactive control based on sEMG signals

Continuous Motion Estimation of Knee Joint Based on a Parameter Self-Updating Mechanism Model

Contact Info

Product

Resources

About