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Human Motion Prediction via Dual-Attention and Multi-Granularity Temporal Convolutional Networks
Intelligent devices, which significantly improve the quality of life and work efficiency, are now widely integrated into people’s daily lives and work. A precise understanding and analysis of human motion is essential for achieving harmonious coexistence and efficient interaction between intelligent...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10304512/ https://www.ncbi.nlm.nih.gov/pubmed/37420819 http://dx.doi.org/10.3390/s23125653 |
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author | Huang, Biaozhang Li, Xinde |
author_facet | Huang, Biaozhang Li, Xinde |
author_sort | Huang, Biaozhang |
collection | PubMed |
description | Intelligent devices, which significantly improve the quality of life and work efficiency, are now widely integrated into people’s daily lives and work. A precise understanding and analysis of human motion is essential for achieving harmonious coexistence and efficient interaction between intelligent devices and humans. However, existing human motion prediction methods often fail to fully exploit the dynamic spatial correlations and temporal dependencies inherent in motion sequence data, which leads to unsatisfactory prediction results. To address this issue, we proposed a novel human motion prediction method that utilizes dual-attention and multi-granularity temporal convolutional networks (DA-MgTCNs). Firstly, we designed a unique dual-attention (DA) model that combines joint attention and channel attention to extract spatial features from both joint and 3D coordinate dimensions. Next, we designed a multi-granularity temporal convolutional networks (MgTCNs) model with varying receptive fields to flexibly capture complex temporal dependencies. Finally, the experimental results from two benchmark datasets, Human3.6M and CMU-Mocap, demonstrated that our proposed method significantly outperformed other methods in both short-term and long-term prediction, thereby verifying the effectiveness of our algorithm. |
format | Online Article Text |
id | pubmed-10304512 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-103045122023-06-29 Human Motion Prediction via Dual-Attention and Multi-Granularity Temporal Convolutional Networks Huang, Biaozhang Li, Xinde Sensors (Basel) Article Intelligent devices, which significantly improve the quality of life and work efficiency, are now widely integrated into people’s daily lives and work. A precise understanding and analysis of human motion is essential for achieving harmonious coexistence and efficient interaction between intelligent devices and humans. However, existing human motion prediction methods often fail to fully exploit the dynamic spatial correlations and temporal dependencies inherent in motion sequence data, which leads to unsatisfactory prediction results. To address this issue, we proposed a novel human motion prediction method that utilizes dual-attention and multi-granularity temporal convolutional networks (DA-MgTCNs). Firstly, we designed a unique dual-attention (DA) model that combines joint attention and channel attention to extract spatial features from both joint and 3D coordinate dimensions. Next, we designed a multi-granularity temporal convolutional networks (MgTCNs) model with varying receptive fields to flexibly capture complex temporal dependencies. Finally, the experimental results from two benchmark datasets, Human3.6M and CMU-Mocap, demonstrated that our proposed method significantly outperformed other methods in both short-term and long-term prediction, thereby verifying the effectiveness of our algorithm. MDPI 2023-06-16 /pmc/articles/PMC10304512/ /pubmed/37420819 http://dx.doi.org/10.3390/s23125653 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Huang, Biaozhang Li, Xinde Human Motion Prediction via Dual-Attention and Multi-Granularity Temporal Convolutional Networks |
title | Human Motion Prediction via Dual-Attention and Multi-Granularity Temporal Convolutional Networks |
title_full | Human Motion Prediction via Dual-Attention and Multi-Granularity Temporal Convolutional Networks |
title_fullStr | Human Motion Prediction via Dual-Attention and Multi-Granularity Temporal Convolutional Networks |
title_full_unstemmed | Human Motion Prediction via Dual-Attention and Multi-Granularity Temporal Convolutional Networks |
title_short | Human Motion Prediction via Dual-Attention and Multi-Granularity Temporal Convolutional Networks |
title_sort | human motion prediction via dual-attention and multi-granularity temporal convolutional networks |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10304512/ https://www.ncbi.nlm.nih.gov/pubmed/37420819 http://dx.doi.org/10.3390/s23125653 |
work_keys_str_mv | AT huangbiaozhang humanmotionpredictionviadualattentionandmultigranularitytemporalconvolutionalnetworks AT lixinde humanmotionpredictionviadualattentionandmultigranularitytemporalconvolutionalnetworks |