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Interaction and psychological characteristics of art teaching based on Openpose and Long Short-Term Memory network
As living standards improve, people’s demand for appreciation and learning of art is growing gradually. Unlike the traditional learning model, art teaching requires a specific understanding of learners’ psychology and controlling what they have learned so that they can create new ideas. This article...
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
PeerJ Inc.
2023
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Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280430/ https://www.ncbi.nlm.nih.gov/pubmed/37346712 http://dx.doi.org/10.7717/peerj-cs.1285 |
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author | Qi, Chen |
author_facet | Qi, Chen |
author_sort | Qi, Chen |
collection | PubMed |
description | As living standards improve, people’s demand for appreciation and learning of art is growing gradually. Unlike the traditional learning model, art teaching requires a specific understanding of learners’ psychology and controlling what they have learned so that they can create new ideas. This article combines the current deep learning technology with heart rate to complete the action recognition of art dance teaching. The video data processing and recognition are conducted through the Openpose network and graph convolution network. The heart rate data recognition is completed through the Long Short-Term Memory (LSTM) network. The optimal recognition model is established through the data fusion of the two decision levels through the adaptive weight analysis method. The experimental results show that the accuracy of the classification fusion model is better than that of the single-mode recognition method, which is improved from 85.0% to 97.5%. The proposed method can evaluate the heart rate while ensuring high accuracy recognition. The proposed research can help analyze dance teaching and provide a new idea for future combined research on teaching interaction. |
format | Online Article Text |
id | pubmed-10280430 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | PeerJ Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-102804302023-06-21 Interaction and psychological characteristics of art teaching based on Openpose and Long Short-Term Memory network Qi, Chen PeerJ Comput Sci Algorithms and Analysis of Algorithms As living standards improve, people’s demand for appreciation and learning of art is growing gradually. Unlike the traditional learning model, art teaching requires a specific understanding of learners’ psychology and controlling what they have learned so that they can create new ideas. This article combines the current deep learning technology with heart rate to complete the action recognition of art dance teaching. The video data processing and recognition are conducted through the Openpose network and graph convolution network. The heart rate data recognition is completed through the Long Short-Term Memory (LSTM) network. The optimal recognition model is established through the data fusion of the two decision levels through the adaptive weight analysis method. The experimental results show that the accuracy of the classification fusion model is better than that of the single-mode recognition method, which is improved from 85.0% to 97.5%. The proposed method can evaluate the heart rate while ensuring high accuracy recognition. The proposed research can help analyze dance teaching and provide a new idea for future combined research on teaching interaction. PeerJ Inc. 2023-03-21 /pmc/articles/PMC10280430/ /pubmed/37346712 http://dx.doi.org/10.7717/peerj-cs.1285 Text en © 2023 Qi https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited. |
spellingShingle | Algorithms and Analysis of Algorithms Qi, Chen Interaction and psychological characteristics of art teaching based on Openpose and Long Short-Term Memory network |
title | Interaction and psychological characteristics of art teaching based on Openpose and Long Short-Term Memory network |
title_full | Interaction and psychological characteristics of art teaching based on Openpose and Long Short-Term Memory network |
title_fullStr | Interaction and psychological characteristics of art teaching based on Openpose and Long Short-Term Memory network |
title_full_unstemmed | Interaction and psychological characteristics of art teaching based on Openpose and Long Short-Term Memory network |
title_short | Interaction and psychological characteristics of art teaching based on Openpose and Long Short-Term Memory network |
title_sort | interaction and psychological characteristics of art teaching based on openpose and long short-term memory network |
topic | Algorithms and Analysis of Algorithms |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10280430/ https://www.ncbi.nlm.nih.gov/pubmed/37346712 http://dx.doi.org/10.7717/peerj-cs.1285 |
work_keys_str_mv | AT qichen interactionandpsychologicalcharacteristicsofartteachingbasedonopenposeandlongshorttermmemorynetwork |