Ke Xu
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Ke Xu has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Imran Khan
Department of Economics, The University of Haripur, Haripur, Pakistan
1 shared publication - Luhao Cao
Hunan University of Technology and Business, School of Digital Media Engineering and Humanities, Changsha, 410205, China
1 shared publication - Mohd Ziaur Rehman
Department of Finance, College of Business Administration, King Saud University, Riyadh, Saudi Arabia
1 shared publication Zekai Nie
1 shared publication- Zekai Nie
Faculty of Business and Communications, INTI International University, Selangor, Malaysia
1 shared publication - Zhenlin Chen
Graduate School, Lingnan University, Hong Kong 999077, China
1 shared publication
Latest Publications
- 24 January 2026
- Published by Elsevier in Environmental Research
- Vol. 295, 123873
Mining rents, climate extremes, and water stress in China: An econometric assessment of emissions and water-related climate vulnerability
- 24 January 2026
- Published by Elsevier in Environmental Research
- Vol. 295, 123873
Mining rents, climate extremes, and water stress in China: An econometric assessment of emissions and water-related climate vulnerability
- Open Access
- 28 September 2025
- Published by Combinatorial Press in Journal of Combinatorial Mathematics and Combinatorial Computing
- Vol. 127, 51-73
Athlete movement recognition based on deep learning: Exploration combined with biodynamics
Sports movement recognition is vital for performance assessment, training optimization, and injury prevention, but manual observation is slow and inconsistent. We propose a compact framework that fuses deep learning with biodynamic analysis: convolutional neural networks (CNNs) extract spatial cues from video, a biodynamic encoder derives joint angles, torques, velocities, and forces, and temporal convolutional networks (TCNs) capture sequential dependencies. Using a simulated multimodal dataset of athletic activities, our method outperforms baseline CNN and LSTM models, achieving higher precision (91.5), recall (93.2), and accuracy (92.7). Gains are largest for complex biomechanics (e.g., throwing, kicking), with up to a 10% accuracy increase from biodynamic integration. These results highlight the value of multimodal fusion and provide a scalable path toward real-time, AI-driven sports performance monitoring, with potential extensions to niche sports (fencing, gymnastics, pole vaulting, javelin).
- 28 September 2025
- Published by Combinatorial Press in Journal of Combinatorial Mathematics and Combinatorial Computing
- Vol. 127, 51-73
Open AccessAthlete movement recognition based on deep learning: Exploration combined with biodynamics
Sports movement recognition is vital for performance assessment, training optimization, and injury prevention, but manual observation is slow and inconsistent. We propose a compact framework that fuses deep learning with biodynamic analysis: convolutional neural networks (CNNs) extract spatial cues from video, a biodynamic encoder derives joint angles, torques, velocities, and forces, and temporal convolutional networks (TCNs) capture sequential dependencies. Using a simulated multimodal dataset of athletic activities, our method outperforms baseline CNN and LSTM models, achieving higher precision (91.5), recall (93.2), and accuracy (92.7). Gains are largest for complex biomechanics (e.g., throwing, kicking), with up to a 10% accuracy increase from biodynamic integration. These results highlight the value of multimodal fusion and provide a scalable path toward real-time, AI-driven sports performance monitoring, with potential extensions to niche sports (fencing, gymnastics, pole vaulting, javelin).