Evidence map›Paper›PMID 35634076›Full record

ArticleComputational intelligence and neuroscience2022

System Design and Simulation for Square Dance Movement Monitoring Based on Machine Learning.

Ping Lei

Abstract read
In one paragraph

Article in Computational intelligence and neuroscience, 2022. 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

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

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

1 author.

Ping LeiSchool of Physical Education, Chengdu Normal University, Chengdu 611130, Sichuan, China.ORCID https://orcid.org/0000-0003-0964-3574

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Since the reform and opening, China's economy has grown rapidly, and people's living standards have improved significantly. As one of the most effective ways to implement national fitness, square dance has gradually become the main lifestyle of urban communities, an important part of China's sports construction, and an important indicator to reflect the fitness of the masses and the construction of a well-off society in an all-round way. On the other hand, with the rapid development of internet of things technology, many people can use intelligent bracelets based on machine learning technology to realize motion detection. This technology is also applicable in square dance, which is of great significance to exercise and protect health. This paper first reviews the research status of the internet of things communication protocol and cloud platform, then introduces and analyzes the MQTT communication protocol and Netty high-performance network framework, and studies the integration technology of the internet of things and machine learning. Then, according to the characteristics of the internet of things, a scheme to realize data preprocessing is proposed. The value to be completed is calculated based on the correlation of other attributes corresponding to the k-nearest neighbor model (KNN) and the regression model. Finally, the machine learning algorithm is used to train the results of the three models to obtain the final filling value. The whole scheme design allows the machine learning algorithm to obtain relatively high-quality data in the internal environment. This paper designs a sports monitoring data system for square dance by combining machine learning and internet of things technology, so as to promote national fitness.

Indexed as

DancingAlgorithmsComputer SimulationHumansMachine LearningMovement

Identifiers

PMID35634076
PMCPMC9135547

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.