A Long Short Term Memory Network Based on Surface Electromyography for Continuous Estimation of Elbow Joint Angle

Chai, Yuanyuan; Liu, Keping; Sun, Zhongbo; Wang, Gang; Shi, Tian

doi:10.1109/ddcls49620.2020.9275122

Cited by 3 publications

(2 citation statements)

References 28 publications

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“…On the other hand, studies [75] and [76] integrated the NARX model with the MLP, ElmanNN, and the Adaptive Neuro-Fuzzy Inference System (ANFIS) from [77] for predictions based on TD features, ultimately proving the superior predictive performance of the ANFIS-NARX model. d) Deep Learning: Studies [78] and [79] employed LSTM based on TD features for precise joint angle predictions. Study [80] showed the superiority of CNN-LSTM over individual CNN and LSTM models, emphasizing the importance of establishing long-term contextual dependencies among extracted advanced features.…”

Section: ) Elbowmentioning

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.

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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.

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Section: ) Elbowmentioning

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.

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“…Meanwhile, in [17], Li et al designed the iterative learning controller based on the RBFNN, which accomplished the intention recognition of lower limbs, to realize the gait tracking of the lower limb rehabilitation robot under the condition of noise pollution. In [18], a long short term memory network (LSTM) has been applied by Chai et al to estimate the elbow joint angle from the sEMG signals.…”

Section: Introductionmentioning

confidence: 99%

Intention Recognition of Elbow Joint based on sEMG Using Adaptive Fuzzy Neural Network

Liu

Teng

et al. 2020

2020 5th International Conference on Mechanical, Control and Computer Engineering (ICMCCE)

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In this paper, the adaptive fuzzy neural network (AFNN) based on the surface electromyography (sEMG) for estimating the elbow joint angle is established and investigated from the perspective of rapidity and accuracy. In addition, back propagation neural network (BPNN) and artificial neural network of radial basis function (RBFNN), as the classical method for data forecasting, have been applied to estimate the elbow joint angle for comparing with AFNN. Ultimately, the experimental simulation and result analysis demonstrate that the rapidity and accuracy of AFNN is superior to BPNN and RBFNN.

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Unconstrained Lower Limb Motion Recognition by Angle Assisted Annotation-Based Surface EMG Sequential Labelling

Tao

2021

2021 IEEE 10th Data Driven Control and Learning Systems Conference (DDCLS)

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A Long Short Term Memory Network Based on Surface Electromyography for Continuous Estimation of Elbow Joint Angle

Cited by 3 publications

References 28 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

Intention Recognition of Elbow Joint based on sEMG Using Adaptive Fuzzy Neural Network

Unconstrained Lower Limb Motion Recognition by Angle Assisted Annotation-Based Surface EMG Sequential Labelling

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