Continuous sEMG estimation method of upper limb shoulder elbow torque based on CNN-LSTM

Li, Cunxin; Zhang, Xiaodong; Li, Hanzhe; Xu, Haibo

doi:10.1109/robio54168.2021.9739373

Cited by 4 publications

(1 citation statement)

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“…Both [179] and [180] verified that Bi-LSTM not only outperformed MLP, CNN, LSTM, and GUR but also effectively addressed the issues of asynchrony and tremors between sEMG and joint angles caused by muscle deformation, and with generalizability to untrained new data. Studies [181] and [182] respectively input TFD features and neural activation into CNN-LSTM for prediction, outperforming the SVR and CNN, yet requiring improvement for generalization across days. Lastly, study [183] replaced the CNN in CNN-LSTM with Short-Connection AE (SCA), achieving better performance and generalization than MLP and CNN.…”

Section: ) Elbow-shoulder Joints A) Traditional Neural Networkmentioning

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: ) Elbow-shoulder Joints A) Traditional Neural Networkmentioning

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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Prediction of Elbow Torque Using Improved African Vultures Optimization Algorithm in Neuromusculoskeletal Model

Xia,

Liu,

Zhu

et al. 2023

Lecture Notes in Computer Science

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EMG-to-torque models for exoskeleton assistance: a framework for the evaluation ofin situcalibration

Quesada,

Verdel,

Bruneau

et al. 2024

Preprint

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In the field of robotic exoskeleton control, it is critical to accurately predict the intention of the user. While surface electromyography (EMG) holds the potential for such precision, current limitations arise from the absence of robust EMG-to-torque model calibration procedures and a universally accepted model. This paper introduces a practical framework for calibrating and evaluating EMG-to-torque models, accompanied by a novel nonlinear model. The framework includes an in situ procedure that involves generating calibration trajectories and subsequently evaluating them using standardized criteria. A comprehensive assessment on a dataset with 17 participants, encompassing single-joint and multi-joint conditions, suggests that the novel model outperforms the others in terms of accuracy while conserving computational efficiency. This contribution introduces an efficient model and establishes a versatile framework for EMG-to-torque model calibration and evaluation, complemented by a dataset made available. This further lays the groundwork for future advancements in EMG-based exoskeleton control and human intent detection.

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Continuous sEMG estimation method of upper limb shoulder elbow torque based on CNN-LSTM

Cited by 4 publications

References 0 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

Prediction of Elbow Torque Using Improved African Vultures Optimization Algorithm in Neuromusculoskeletal Model

EMG-to-torque models for exoskeleton assistance: a framework for the evaluation ofin situcalibration

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