Evidence map›Paper›PMID 40486205›Full record

ArticleFrontiers in bioengineering and biotechnology2025

Research on rehabilitation robot control based on port-Hamiltonian systems and fatigue dissipation port compensation.

Jingjing Li, Zhen Chen, Jian Li, Hongyu Yan, Zhen Li, Minshan Feng, Jiawen Zhan, Liwei Shao

Abstract read
In one paragraph

Article in Frontiers in bioengineering and biotechnology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Jingjing LiSchool of Automation, Beijing Institute of Technology, Beijing, China.
Zhen ChenSchool of Automation, Beijing Institute of Technology, Beijing, China.
Jian LiSchool of Automation, Beijing Institute of Technology, Beijing, China.
Hongyu YanSchool of Automation, Beijing Institute of Technology, Beijing, China.
Zhen LiSchool of Automation, Beijing Institute of Technology, Beijing, China.
Minshan FengWangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Jiawen ZhanWangjing Hospital, China Academy of Chinese Medical Sciences, Beijing, China.
Liwei ShaoGreater Bay Area Innovation Institute, Beijing Institute of Technology, Zhuhai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Upper-limb rehabilitation robots have been demonstrated to effectively promote motor recovery in stroke patients. However, in active training modes, control instability may be induced by the nonlinear and time-varying characteristics of muscle fatigue, increasing the risks of physical human-robot interaction and ultimately limiting rehabilitation outcomes. Methods: A novel control strategy within the port-Hamiltonian framework, incorporating a dynamic muscle fatigue model. Fatigue levels were assessed in real time using surface electromyography (sEMG) signals and mapped to damping parameters in joint space, enabling the port-based modeling of fatigue-related energy dissipation. A hierarchical control architecture was constructed, consisting of outer-loop admittance control and inner-loop energy shaping. Results: Theoretical analysis confirmed that the closed-loop passivity of the system was preserved and stability was ensured. Experimental validation further showed that, compared to fixed damping parameters, the proposed fatigue compensation approach reduced muscle fatigue accumulation by 45% and increased training duration by 40%. Discussion: The proposed fatigue-adaptive control framework was shown to enhance the safety, effectiveness, and physiological adaptability of rehabilitation training. The integration of real-time sEMG feedback and port-Hamiltonian modeling offers a promising solution for personalized robotic rehabilitation.

Indexed as

human-robot interaction stabilitymuscle fatigue modelingpassivity-based controlport-Hamiltonian systemupper-limb rehabilitation robot

Identifiers

PMID40486205
PMCPMC12141283

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.