IoT-enhanced multi-attention and lightweight feature integration for human pose estimation in motion training systems
Human pose estimation is widely used in intelligent sports training, rehabilitation assistance, and human–computer interaction, providing precise motion feedback and training guidance. However, existing methods suffer from keypoint localization errors and insufficient global coherence in complex bac...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-08-01
|
| Series: | Alexandria Engineering Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1110016825005708 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849229333980250112 |
|---|---|
| author | Junwen Chen Jian Yang Zhiqun Wang |
| author_facet | Junwen Chen Jian Yang Zhiqun Wang |
| author_sort | Junwen Chen |
| collection | DOAJ |
| description | Human pose estimation is widely used in intelligent sports training, rehabilitation assistance, and human–computer interaction, providing precise motion feedback and training guidance. However, existing methods suffer from keypoint localization errors and insufficient global coherence in complex backgrounds, occlusions, and high-dynamic motion scenarios. To address these challenges, this paper proposes a hybrid IoT-vision deep learning model for human pose estimation and motion training feedback. IoT-based motion sensors are integrated with vision-based keypoint detection to enhance pose estimation accuracy, particularly in occluded or high-speed movement scenarios. The model employs the LSFE stacked feature extraction module to enhance multi-scale feature adaptability, incorporates the LFAM local attention mechanism (SPAM + CARM) to improve key joint modeling, and introduces the GEAM global enhancement module to ensure keypoint stability and consistency. Additionally, an ECA-based lightweight channel attention mechanism reduces computational complexity while enhancing key feature responses. Experimental results show that the proposed model achieves Mean Accuracy of 0.946 and 0.949 on the LSP and MPII datasets, respectively, with PCK scores of 0.97 and 0.95. This model demonstrates significant improvements over existing methods in real-time performance and robustness, particularly in complex scenarios such as sports training, rehabilitation, and monitoring. |
| format | Article |
| id | doaj-art-cb629594e5124b059f2cefcc9d0e8d48 |
| institution | Kabale University |
| issn | 1110-0168 |
| language | English |
| publishDate | 2025-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Alexandria Engineering Journal |
| spelling | doaj-art-cb629594e5124b059f2cefcc9d0e8d482025-08-22T04:55:15ZengElsevierAlexandria Engineering Journal1110-01682025-08-0112728429510.1016/j.aej.2025.04.074IoT-enhanced multi-attention and lightweight feature integration for human pose estimation in motion training systemsJunwen Chen0Jian Yang1Zhiqun Wang2Jiangxi University of Finance and Economics, Nanchang, Jiangxi 330000, ChinaDepartment of Physical Education, Zhejiang University of Finance & Economics, Hangzhou, 310018, China; Corresponding author.School of Electronic Information, HuZhou College, HuZhou, 313000, ChinaHuman pose estimation is widely used in intelligent sports training, rehabilitation assistance, and human–computer interaction, providing precise motion feedback and training guidance. However, existing methods suffer from keypoint localization errors and insufficient global coherence in complex backgrounds, occlusions, and high-dynamic motion scenarios. To address these challenges, this paper proposes a hybrid IoT-vision deep learning model for human pose estimation and motion training feedback. IoT-based motion sensors are integrated with vision-based keypoint detection to enhance pose estimation accuracy, particularly in occluded or high-speed movement scenarios. The model employs the LSFE stacked feature extraction module to enhance multi-scale feature adaptability, incorporates the LFAM local attention mechanism (SPAM + CARM) to improve key joint modeling, and introduces the GEAM global enhancement module to ensure keypoint stability and consistency. Additionally, an ECA-based lightweight channel attention mechanism reduces computational complexity while enhancing key feature responses. Experimental results show that the proposed model achieves Mean Accuracy of 0.946 and 0.949 on the LSP and MPII datasets, respectively, with PCK scores of 0.97 and 0.95. This model demonstrates significant improvements over existing methods in real-time performance and robustness, particularly in complex scenarios such as sports training, rehabilitation, and monitoring.http://www.sciencedirect.com/science/article/pii/S1110016825005708Human pose estimationMotion training feedbackLightweight attention mechanismMulti-scale feature extractionKeypoint detectionIoT-based sensor fusion |
| spellingShingle | Junwen Chen Jian Yang Zhiqun Wang IoT-enhanced multi-attention and lightweight feature integration for human pose estimation in motion training systems Alexandria Engineering Journal Human pose estimation Motion training feedback Lightweight attention mechanism Multi-scale feature extraction Keypoint detection IoT-based sensor fusion |
| title | IoT-enhanced multi-attention and lightweight feature integration for human pose estimation in motion training systems |
| title_full | IoT-enhanced multi-attention and lightweight feature integration for human pose estimation in motion training systems |
| title_fullStr | IoT-enhanced multi-attention and lightweight feature integration for human pose estimation in motion training systems |
| title_full_unstemmed | IoT-enhanced multi-attention and lightweight feature integration for human pose estimation in motion training systems |
| title_short | IoT-enhanced multi-attention and lightweight feature integration for human pose estimation in motion training systems |
| title_sort | iot enhanced multi attention and lightweight feature integration for human pose estimation in motion training systems |
| topic | Human pose estimation Motion training feedback Lightweight attention mechanism Multi-scale feature extraction Keypoint detection IoT-based sensor fusion |
| url | http://www.sciencedirect.com/science/article/pii/S1110016825005708 |
| work_keys_str_mv | AT junwenchen iotenhancedmultiattentionandlightweightfeatureintegrationforhumanposeestimationinmotiontrainingsystems AT jianyang iotenhancedmultiattentionandlightweightfeatureintegrationforhumanposeestimationinmotiontrainingsystems AT zhiqunwang iotenhancedmultiattentionandlightweightfeatureintegrationforhumanposeestimationinmotiontrainingsystems |