Evidence map›Paper›PMID 40057479›Full record

Trial reportNature communications2025

Power-free knee rehabilitation robot for home-based isokinetic training.

Yanggang Feng, Haoyang Wu, Jiaxin Ren, Wuxiang Zhang, Xiu Jia, Xiuhua Liu, Xingyu Hu, Haoxiang Jing, Yuebing Li, Yuhang Zhao and 13 more

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Nature communications, 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. Review
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

23 authors.

Yanggang Feng *School of Mechanical Engineering and Automation, Beihang University, Beijing, China. yanggangfeng@buaa.edu.cn.ORCID http://orcid.org/0000-0001-6161-3394
Haoyang Wu *School of Mechanical Engineering and Automation, Beihang University, Beijing, China.
Jiaxin RenSchool of Mechanical Engineering and Automation, Beihang University, Beijing, China.
Wuxiang ZhangSchool of Mechanical Engineering and Automation, Beihang University, Beijing, China. zhangwuxiang@buaa.edu.cn.
Xiu JiaCAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Modern Mechanics, University of Science and Technology of China, Hefei, China.
Xiuhua LiuIntelligent Science & Technology Academy Limited of CASIC, Beijing, China.
Xingyu HuSchool of Mechanical Engineering and Automation, Beihang University, Beijing, China.ORCID http://orcid.org/0009-0004-1142-5546
Haoxiang JingSchool of Mechanical Engineering and Automation, Beihang University, Beijing, China.
Yuebing LiSchool of Mechanical Engineering and Automation, Beihang University, Beijing, China.
Yuhang ZhaoSchool of Mechanical Engineering and Automation, Beihang University, Beijing, China.
Ziyan WangSchool of Mechanical Engineering and Automation, Beihang University, Beijing, China.
Xuzhou LangSchool of Mechanical Engineering and Automation, Beihang University, Beijing, China.
Junjia XuSchool of Mechanical Engineering and Automation, Beihang University, Beijing, China.
Yixin ShaoSchool of Mechanical Engineering and Automation, Beihang University, Beijing, China.
Qi SuDepartment of Automation, Shanghai Jiao Tong University, Shanghai, China.ORCID http://orcid.org/0000-0002-6490-686X
Yuanmingfei ZhangPeking University Third Hospital, Beijing, China.
Mouwang ZhouPeking University Third Hospital, Beijing, China.
Ke LiuDepartment of Advanced Manufacturing and Robotics, Peking University, Beijing, China.ORCID http://orcid.org/0000-0001-9081-6334
Yong NieWest China Hospital, Sichuan University, Chengdu, China.
Jian WangPeking University Third Hospital, Beijing, China.
Fuzhen YuanPeking University Third Hospital, Beijing, China.
Liu WangCAS Key Laboratory of Mechanical Behavior and Design of Materials, Department of Modern Mechanics, University of Science and Technology of China, Hefei, China. wangliu05@ustc.edu.cn.ORCID http://orcid.org/0000-0001-7014-9976
Xilun DingSchool of Mechanical Engineering and Automation, Beihang University, Beijing, China. xlding@buaa.edu.cn.ORCID http://orcid.org/0000-0002-9752-3937

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Robot-assisted isokinetic training has been widely adopted for knee rehabilitation. However, existing rehabilitation facilities are often heavy, bulky, and extremely energy-consuming, which limits the rehabilitation opportunities only at designated hospitals. In this study, we introduce a highly integrated and lightweight (52 kg) knee rehabilitation robot that can provide home-based isokinetic training without external power. By integrating a motor, torque/angle sensors, control circuit, and energy regeneration circuit into a single driver module, our robot can provide power-free isokinetic training by recycling mechanical work from the trainee. Ten postsurgical subjects were involved in an interventional randomized trial (ChiCTR2300076715, Part I) and the cross-sectional area of trained legs (experimental group) was significantly higher than that of untrained legs (control group). The primary outcomes, muscle growth (quadriceps: 5.93%, hamstrings: 10.27%) and strength improvements (quadriceps: 70%, hamstrings: 84%), achieved with our robots surpass those of existing commercial rehabilitation devices. These findings indicate that our robot presents a viable option for home-based knee rehabilitation, significantly enhancing the accessibility of effective treatment.

Indexed as

Exercise TherapyKneeKnee JointRoboticsAdultFemaleHumansMaleMiddle AgedMuscle StrengthTorqueYoung Adult

Identifiers

PMID40057479
PMCPMC11890594

What OpenQuestion holds

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Registered trials

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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.