A Survey on Mathematical Modeling of Muscle Using for Rehabilitation Systems

Santos, Juliana V. dos; Ramis, Marcos Vincius M.; Oliveira, Vinícius Machado de; Azzolin, Rodrigo Zelir

doi:10.1109/iecon.2019.8927318

Cited by 2 publications

(1 citation statement)

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“…However, due to physiological differences among patients, the generic proportional Hill model derived from extensive cadaveric specimens can lead to significant prediction errors. Therefore, parameter optimization for subject-specific MSK models in the final stage of MB methods is necessary to achieve precise predictions [9], [10].…”

Section: Introductionmentioning

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: Introductionmentioning

confidence: 99%