Evidence map›Paper›PMID 38542548›Full record

ArticleMicromachines2024

Design and Control of Upper Limb Rehabilitation Training Robot Based on a Magnetorheological Joint Damper.

Jintao Zhu, Hongsheng Hu, Wei Zhao, Jiabin Yang, Qing Ouyang

Abstract read
In one paragraph

Article in Micromachines, 2024. 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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0citing papers in PubMed
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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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

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

5 authors.

Jintao ZhuCollege of Mechanical Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
Hongsheng HuCollege of Information Science and Engineering, Jiaxing University, Jiaxing 314001, China.
Wei ZhaoCollege of Information Science and Engineering, Jiaxing University, Jiaxing 314001, China.
Jiabin YangCollege of Information Science and Engineering, Jiaxing University, Jiaxing 314001, China.
Qing OuyangCollege of Information Science and Engineering, Jiaxing University, Jiaxing 314001, China.

Funding

Jiaxing Municipal Science and Technology Project 2020AY10036Natural Science Foundation of China (NSFC) grant funded by the Chinese Government 51805209Qin Shen Scholar Program of Jiaxing University CD70623001Zhejiang Provincial Natural Science Foundation of China LQ22E050018, LGG20E050022Zhejiang Provincial Natural Science Foundation of China LTGY24E050001
6 · The paper itself

Abstract

In recent years, rehabilitation robots have been developed and used in rehabilitation training for patients with hemiplegia. In this paper, a rehabilitation training robot with variable damping is designed to train patients with hemiplegia to recover upper limb function. Firstly, a magnetorheological joint damper (MR joint damper) is designed for the rehabilitation training robot, and its structural design and dynamic model are tested theoretically and experimentally. Secondly, the rehabilitation robot is simplified into a spring-damping system, and the rehabilitation training controller for human movement is designed. The rehabilitation robot dynamically adjusts the excitation current according to the feedback speed and human-machine interaction torque, so that the rehabilitation robot always outputs a stable torque. The magnetorheological joint damper acts as a clutch to transmit torque safely and stably to the robot joint. Finally, the upper limb rehabilitation device is tested. The expected torque is set to 20 N, and the average value of the output expected torque during operation is 20.02 N, and the standard deviation is 0.635 N. The output torque has good stability. A fast (0.5 s) response can be achieved in response to a sudden motor speed change, and the average expected output torque is 20.38 N and the standard deviation is 0.645 N, which can still maintain the stability of the output torque.

Indexed as

magnetorheological joint damperrehabilitation trainingsafety testingtrajectory tracking controlupper limb rehabilitation robot

Identifiers

PMID38542548
PMCPMC10971917

What OpenQuestion holds

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LicenceCC BY
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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.