Grasping force estimation using state-space model and Kalman Filter

Dutra, Bruno Gomes; Silveira, Antonio S.; Pereira, António

doi:10.1016/j.bspc.2021.103036

Cited by 9 publications

(1 citation statement)

References 24 publications

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“…a) State Space Model: Studies [108] and [109] applied the SS model based on the N4SID parameter identification method to predict finger joint angles under different static wrist postures during the mirrored bilateral movement. Additionally, study [110] utilized Recursive Least Squares (RLS) for SS model parameter estimation and the KF for post-processing, ultimately outperforming MLP, NARX, and LDA models.…”

Section: ) Hand 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.

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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: ) Hand Jointsmentioning

confidence: 99%