Evidence map›Paper›PMID 35694572›Full record

ArticleComputational intelligence and neuroscience2022

Application of Image Recognition Based on Wireless Sensors in Dance Teaching System.

Chi Ben

RetractedAbstract readRetracted Publication
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. It has been retracted, and should not be counted.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

1 author.

Chi BenCollege of Wushu and Dance, Shenyang Sport University, Liaoning, Shenyang 110102, China.ORCID https://orcid.org/0000-0001-9332-2997

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the vigorous development of higher education in China, many universities have made great progress in various indicators in recent years. As the number of college students increases year by year, the effect of instruction in the classroom is especially important. The high quality of teaching directly affects the efficiency of students' listening to lectures, and more and more universities are receiving attention. However, the traditional dance classroom education and the one-to-many education model cannot adapt to the development trend of higher art education under the changes of the times and cannot effectively guarantee the quality of classroom education. The development of wireless sensor networks provides practical and feasible technical solutions for the development of dance education systems. Compared with general detection methods, image sensors can provide more real-time and more intuitive on-site information and wirelessly send image information to user terminals. This article describes the classic feature extraction algorithm and proposes a new feature extraction algorithm based on chart filling. The effectiveness of each algorithm is verified through several data sets. Image recognition is carried out by computer, including from computer to image processing, through the computer to recognize objects and various different modes of the target technology. The identification process usually includes several steps. First, the preprocessing of the image is required, then the segmentation of the image is performed, and then the feature extraction and matching are performed. In layman's terms, image recognition hopes to imitate the human heart to read photos. By applying the image recognition technology to the dance education system, changes in the methods and forms of dance education can be stimulated.

Indexed as

AlgorithmsImage Processing, Computer-AssistedChinaHumansStudentsTeachingUniversities

Identifiers

PMID35694572
PMCPMC9177297

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.