Human pose evaluation based on full-domain convolution and LSTM

Zou, Yu; Pan, Zhigeng; Zhou, Xianchun; Wang, Yixuan

Open Access

Human pose evaluation based on full-domain convolution and LSTM

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Oct 17, 2023

Applied Mathematics and Nonlinear Sciences

Volume 9 (2024): Issue 1 (January 2024)

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Published Online: Oct 17, 2023

Received: Dec 26, 2022

Accepted: Apr 27, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00680

Keywords
Full domain convolution, LSTM, Bone structure skeleton, Human pose assessment, Convolutional neural network

This work is licensed under the Creative Commons Attribution 4.0 International License.

Zou, Yu

School of Aritificial Intelligence (School of Future Technology), Nanjing University of Information Science & TechnologyNanjing, China

Science and Technology Office, Jiangsu Open UniversityNanjing, China

Pan, Zhigeng

School of Aritificial Intelligence (School of Future Technology), Nanjing University of Information Science & TechnologyNanjing, China

Zhou, Xianchun

School of Aritificial Intelligence (School of Future Technology), Nanjing University of Information Science & TechnologyNanjing, China

Wang, Yixuan

College of Cultural Management, Communication University of China NanjingNanjing, China

Language:: English

Publication timeframe:: 1 times per year
Journal Subjects:: Life Sciences, Life Sciences, other, Mathematics, Applied Mathematics, General Mathematics, Physics, Physics, other

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Human pose evaluation based on full-domain convolution and LSTM

Yu Zou

Zhigeng Pan

Xianchun Zhou

Yixuan Wang

Published Online: Oct 17, 2023

Received: Dec 26, 2022

Accepted: Apr 27, 2023

DOI: https://doi.org/10.2478/amns.2023.2.00680

KeywordsFull domain convolution, LSTM, Bone structure skeleton, Human pose assessment, Convolutional neural network

© 2023 Yu Zou et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Keywords
Full domain convolution, LSTM, Bone structure skeleton, Human pose assessment, Convolutional neural network