Evidence map›Paper›PMID 34373774›Full record

ArticleJournal of healthcare engineering2021

Wearable Device-Based Smart Football Athlete Health Prediction Algorithm Based on Recurrent Neural Networks.

Qingkun Feng, Yanying Liu, Lijun Wang

RetractedAbstract readRetracted Publication
In one paragraph

Article in Journal of healthcare engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. 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. Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

3 authors.

Qingkun FengInstitute of Physical Education and Health, Zhaoqing University, Zhaoqing 526000, Guangdong, China.
Yanying LiuInstitute of Physical Education and Health, Zhaoqing University, Zhaoqing 526000, Guangdong, China.
Lijun WangInstitute of Physical Education and Health, Yulin Normal University, Yulin 537000, China.ORCID 0000-0003-0498-8645

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

For football players who participate in sports, the word "health" is extremely important. Athletes cannot create their own value in competitive competitions without a strong foundation. Scholars have paid a lot of attention to athlete health this year, and many analysis methods have been proposed, but there have been few studies using neural networks. As a result, this article proposes a novel wearable device-based smart football player health prediction algorithm based on recurrent neural networks. To begin, this article employs wearable sensors to collect health data from football players. The time step data are then fed into a recurrent neural network to extract deep features, followed by the health prediction results. The collected football player health dataset is used in this paper to conduct experiments. The simulation results prove the reliability and superiority of the proposed algorithm. Furthermore, the algorithm presented in this paper can serve as a foundation for the football team's and coaches' scientific training plans.

Indexed as

FootballWearable Electronic DevicesAlgorithmsAthletesHumansNeural Networks, ComputerReproducibility of Results

Identifiers

PMID34373774
PMCPMC8349259

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.