Human posture estimation and action recognition on fitness behavior and fitness
In daily fitness activities, posture recognition and motion capture are extremely important. This study aims to explore skeleton-based action recognition technology and compare the effects of using only skeleton information and fused image features. We introduce an improved spatiotemporal pyramid gr...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-11-01
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| Series: | Alexandria Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016824007609 |
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| _version_ | 1846167141530730496 |
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| author | Ying Zhang Chenqiong Zhao Yuan Yao Chunxiao Wang Guoliang Cai Gang Wang |
| author_facet | Ying Zhang Chenqiong Zhao Yuan Yao Chunxiao Wang Guoliang Cai Gang Wang |
| author_sort | Ying Zhang |
| collection | DOAJ |
| description | In daily fitness activities, posture recognition and motion capture are extremely important. This study aims to explore skeleton-based action recognition technology and compare the effects of using only skeleton information and fused image features. We introduce an improved spatiotemporal pyramid graph convolutional network, which enhances the performance of the model by introducing edge importance scores and multi-level feature representation when dealing with specific action recognition tasks. The proposed model is tested on the UW-IOM and TUM kitchen datasets to verify its applicability in real-world scenarios, the mAP of UW-IOM reached 85.89, achieving the best result and proving the effectiveness of the model. Through experiments, we confirm that in certain cases, the accuracy of skeleton information is the key to improving action recognition performance, while in scenarios where image information is not rich or skeleton information is inaccurate, the fusion of image features can provide performance supplements. In addition, we demonstrate the advantages of our method by comparing with existing methods. |
| format | Article |
| id | doaj-art-c1c9d9e480584cd5bb72f2665dc4fadf |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-c1c9d9e480584cd5bb72f2665dc4fadf2024-11-15T06:11:11ZengElsevierAlexandria Engineering Journal1110-01682024-11-01107434442Human posture estimation and action recognition on fitness behavior and fitnessYing Zhang0Chenqiong Zhao1Yuan Yao2Chunxiao Wang3Guoliang Cai4Gang Wang5School of Sports Human Science, Harbin Institute Of Physical Education, Harbin, 150008, ChinaSchool of Sports Human Science, Harbin Institute Of Physical Education, Harbin, 150008, ChinaSchool of Sports Human Science, Harbin Institute Of Physical Education, Harbin, 150008, ChinaSchool of Sports Human Science, Harbin Institute Of Physical Education, Harbin, 150008, ChinaSchool of Sports Human Science, Harbin Institute Of Physical Education, Harbin, 150008, China; Corresponding author.School of Computing and Data Engineering, NingboTech University, Ningbo, 315100, China; Department of Bioengineering, Imperial College London, London, SW7 2AZ, UKIn daily fitness activities, posture recognition and motion capture are extremely important. This study aims to explore skeleton-based action recognition technology and compare the effects of using only skeleton information and fused image features. We introduce an improved spatiotemporal pyramid graph convolutional network, which enhances the performance of the model by introducing edge importance scores and multi-level feature representation when dealing with specific action recognition tasks. The proposed model is tested on the UW-IOM and TUM kitchen datasets to verify its applicability in real-world scenarios, the mAP of UW-IOM reached 85.89, achieving the best result and proving the effectiveness of the model. Through experiments, we confirm that in certain cases, the accuracy of skeleton information is the key to improving action recognition performance, while in scenarios where image information is not rich or skeleton information is inaccurate, the fusion of image features can provide performance supplements. In addition, we demonstrate the advantages of our method by comparing with existing methods.http://www.sciencedirect.com/science/article/pii/S1110016824007609FitnessGesture recognitionSkeletonMotion captureFusionSmart equipment |
| spellingShingle | Ying Zhang Chenqiong Zhao Yuan Yao Chunxiao Wang Guoliang Cai Gang Wang Human posture estimation and action recognition on fitness behavior and fitness Alexandria Engineering Journal Fitness Gesture recognition Skeleton Motion capture Fusion Smart equipment |
| title | Human posture estimation and action recognition on fitness behavior and fitness |
| title_full | Human posture estimation and action recognition on fitness behavior and fitness |
| title_fullStr | Human posture estimation and action recognition on fitness behavior and fitness |
| title_full_unstemmed | Human posture estimation and action recognition on fitness behavior and fitness |
| title_short | Human posture estimation and action recognition on fitness behavior and fitness |
| title_sort | human posture estimation and action recognition on fitness behavior and fitness |
| topic | Fitness Gesture recognition Skeleton Motion capture Fusion Smart equipment |
| url | http://www.sciencedirect.com/science/article/pii/S1110016824007609 |
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