Evidence map›Paper›PMID 41470552›Full record

ArticleMicromachines2025

Muscle Strength Training and Monitoring Device Based on Triboelectric Nanogenerator for Knee Joint Surgery.

Jing Liu, Yi Zhang, Xia Liu, Chenming Sun, Youquan Wang

Abstract read
In one paragraph

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

0numbers the graph read from it
0cells of the map it votes in
0citing 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

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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

Who cites it

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.

Jing LiuFaculty of Rehabilitation Medicine, Jining Medical University, Jining 272067, China.
Yi ZhangFaculty of Electrical and Electronic Engineering, Changchun University of Technology, Changchun 130012, China.
Xia LiuFaculty of Rehabilitation Medicine, Jining Medical University, Jining 272067, China.
Chenming SunFaculty of Rehabilitation Medicine, Jining Medical University, Jining 272067, China.
Youquan WangFaculty of Rehabilitation Medicine, Jining Medical University, Jining 272067, China.

Funding

Art Science Key Topic of Shandong Province program L2023Z04190577Research center of Shandong Intelligent rehabilitation Engineering funded Lu education word [2022] 2TCM Science and Technology of Shandong Province Program 2021Q067
6 · The paper itself

Abstract

At present, there are some devices for muscle strength training after knee surgery, such as elastic bands and isokinetic muscle strength training instruments, but most of them are expensive or cannot monitor training progress. Triboelectric nanogenerators (TENGs) have proven to be reliable self-sensing devices. There have been some applications in the field of rehabilitation, but few have been used for muscle strength training. Our team has innovatively applied the TENG self-sensing device to the self-rehabilitation management of the knee joint post-surgery. We have developed the "Triboelectric Nanogenerator for Muscle Strength Training of Knee Joint after Surgery" (MSTKJS-TENG), which is significantly more integrated than traditional instruments (volume: 120 mm × 100 mm × 100 mm) and can real-time track the number and quality of movements completed by patients during muscle strength training. The development of this device has made up for the deficiencies of traditional instruments. It can assist medical staff in remotely evaluating the recovery of patients' postoperative muscle strength to a certain extent, thereby adjusting training intensity in a timely manner and providing personalized guidance. Meanwhile, the research on this device provides effective technical support and innovation for the development of smart rehabilitation medicine.

Indexed as

knee joint operationmuscle strength trainingself-rehabilitation managementself-sensingtriboelectric nanogenerator

Identifiers

PMID41470552
PMCPMC12735041

What OpenQuestion holds

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LicenceCC BY
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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.