Evidence map›Paper›PMID 40462770›Full record

ArticleApplied bionics and biomechanics2025

Dynamic Modeling and Simulation Analysis of an Arm Rehabilitation Robot With Mobile Platform.

Tao Gong, Yuanyuan Lin, Yufeng Wang, Wenbin Wang, Wei Chen, Jiancheng Charles Ji

Abstract read
In one paragraph

Article in Applied bionics and biomechanics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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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

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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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

6 authors.

Tao GongInstitute of Intelligent Manufacturing, Shenzhen Polytechnic University, 4089 Shahe West Road, Shenzhen 518055, China.
Yuanyuan LinMedical Insurance Department, Weifang Hospital of Traditional Chinese Medicine, 1055 Weizhou Road, Weifang 261042, China.
Yufeng WangRehabilitation Medicine Department, People's Hospital of Gaoxin, 768 Fudong Road, Weifang 261000, China.
Wenbin WangInstitute of Intelligent Manufacturing, Shenzhen Polytechnic University, 4089 Shahe West Road, Shenzhen 518055, China.
Wei ChenInstitute of Intelligent Manufacturing, Shenzhen Polytechnic University, 4089 Shahe West Road, Shenzhen 518055, China.ORCID https://orcid.org/0009-0007-8838-510X
Jiancheng Charles JiInstitute of Intelligent Manufacturing, Shenzhen Polytechnic University, 4089 Shahe West Road, Shenzhen 518055, China.ORCID https://orcid.org/0000-0002-4401-1487

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In response to the ever-increasing demand of upper limb rehabilitation training and function improvement, a novel arm rehabilitation robot with mobile platform, which can move on the table, is designed to facilitate the upper limbs strength training via the passive force field. The proposed robot provides three passive degrees of freedom (plane motion on the table and rotation around the vertical axis), which can fulfill the robot-aided trajectory training for upper limbs, such as shoulder, elbow, and wrist flexion/extension. Meanwhile, changed force field was established on the table via three elastic ropes. The user first connects the robot by the grab handle and arm support, then the screen displays the reference trajectory (RT) and notifies the user to manipulate the robot to draw the trajectory, the actual trajectory is calculated via the sensors and displayed to feed the user to adjust the muscular strength. In this paper, the reference trajectories, corresponding angular velocity of the wheels and the distribution of force field are analyzed via the dynamic modeling. Simulation studies are carried out to analyze the effectiveness of the theoretical model, kinematic performance, and distribution of force field. The path tracking simulation results showed that the peak error was 1.2 mm for the "∞" curve and 0.9 mm for the "O" curve. The force analysis results showed that the robot can generate the circinate force field and the maximum force of the whole workspace was 9.8 N with the elastic rigidity was 20 N/m. Furthermore, the maximum force was 11.02 N and the minimum force was 6.48 N when the robot moved along the of the circular path. These results demonstrate that the robot can generate appropriate force field to facilitate the motor training and paved the way to interventional therapy of the robot in the future. We will optimize the structure parameters and assemble a prototype to test the performance of the robot.

Identifiers

PMID40462770
PMCPMC12133355

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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.