Evidence map›Paper›PMID 38632997›Full record

ArticleiScience2024

Machine learning-assisted novel recyclable flexible triboelectric nanogenerators for intelligent motion.

Yuzhang Wen, Fengxin Sun, Zhenning Xie, Mengqi Zhang, Zida An, Bing Liu, Yuning Sun, Fei Wang, Yupeng Mao

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in iScience, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
  5. AI-Driven TENGs for Self-Powered Smart Sensors and Intelligent Devices.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025
    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

9 authors.

Yuzhang WenPhysical Education Department, Northeastern University, Shenyang 110819, China.
Fengxin SunPhysical Education Department, Northeastern University, Shenyang 110819, China.
Zhenning XiePhysical Education Department, Northeastern University, Shenyang 110819, China.
Mengqi ZhangPhysical Education Department, Northeastern University, Shenyang 110819, China.
Zida AnFaculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China.
Bing LiuCriminal Investigation Police University of China, Shenyang 110035, China.
Yuning SunPhysical Education Department, Northeastern University, Shenyang 110819, China.
Fei WangFaculty of Robot Science and Engineering, Northeastern University, Shenyang 110819, China.
Yupeng MaoPhysical Education Department, Northeastern University, Shenyang 110819, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In the smart era, big data analysis based on sensor units is important in intelligent motion. In this study, a dance sports and injury monitoring system (DIMS) based on a recyclable flexible triboelectric nanogenerator (RF-TENG) sensor module, a data processing hardware module, and an upper computer intelligent analysis module are developed to promote intelligent motion. The resultant RF-TENG exhibits an ultra-fast response time of 17 ms, coupled with robust stability demonstrated over 4200 operational cycles, with 6% variation in output voltage. The DIMS enables immersive training by providing visual feedback on sports status and interacting with virtual games. Combined with machine learning (K-nearest neighbor), good classification results are achieved for ground-jumping techniques. In addition, it shows some potential in sports injury prediction (i.e., ankle sprains, knee hyperextension). Overall, the sensing system designed in this study has broad prospects for future applications in intelligent motion and healthcare.

Indexed as

Computer scienceHealth sciencesMaterials sciencePhysics

Identifiers

PMID38632997
PMCPMC11022051

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.