Li, Jie and Wang, Jiaxin and Qiu, Sen and Liu, Xiaofeng and Li, Jianqing and Xiang, Wentao and Liu, Bin and Zhu, Songsheng and Kiong, Loo Chu and Cangelosi, Angelo and Fortino, Giancarlo (2025) Smart Swimming Training: Wearable Body Sensor Networks Empower Technical Evaluation of Competitive Swimming. IEEE Internet of Things Journal, 12 (4). pp. 4448-4465. ISSN 2327-4662, DOI https://doi.org/10.1109/JIOT.2024.3485232.
Full text not available from this repository.Abstract
The combination of wearable sensors and competitive sports provides quantitative information for scientific training, effectively assisting athletes in improving their athletic performance. This study presents a technical framework for athletic sports assessment in competitive swimming based on body-area sensor networks. In our approach, wearable inertial sensor nodes are placed on specific body parts of the athletes to capture motion data during different competitive swimming strokes. Multiwearable inertial sensor nodes are worn on specific body parts of athletes for real-time monitoring motion data during training sessions. A motion intensity detection-based error-state-Kalman-filter algorithm is proposed for multisensor data fusion. Additionally, through kinematic statistical analysis, the characteristics of joint motion during training are clearly explained. Furthermore, a deep learning network that fuses sensor time series and human skeleton graphs is proposed for different stroke phase segmentation, enabling quantitative measurement of motion phases, and several baseline classifiers are chosen for comparison to validate the robustness of our phase segmentation method. We also investigate the sensor combination selection issue during the phase segmentation process to determine the optimal sensor configuration. Our approach provides a scientific solution for the integration of wearable sensors and competitive sports, contributing to the high-quality development of the next generation of smart sports.
| Item Type: | Article |
|---|---|
| Funders: | National Natural Science Foundation of China (NSFC) (62203150) ; (62276090), National Key Research & Development Program of China (2022YFC2405600), Jiangsu Key Research and Development Plan (BE2022160), Frontier-Leading Technology Basic Research Special Project in Jiangsu Province (BK20192004), International (Regional) Cooperation Projects in Jiangsu Province (BZ2024061), Changzhou SciTech Program (CJ20220051) |
| Uncontrolled Keywords: | Body sensor network (BSN); competitive swimming; motion capture; motion capture; multisensor data fusion; multisensor data fusion; phase segmentation; phase segmentation; phase segmentation |
| Subjects: | Q Science > QA Mathematics > QA75 Electronic computers. Computer science T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Computer Science & Information Technology > Department of Artificial Intelligence |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 30 Apr 2025 07:01 |
| Last Modified: | 30 Apr 2025 07:01 |
| URI: | http://eprints.um.edu.my/id/eprint/47878 |
Actions (login required)
 |
View Item |
