CV

Academic Appointment

Researcher, Center for Computational Sciences (CCS), University of Tsukuba, Japan
April 2025 – Present

Research on computer vision and machine learning for video-based clinical gait assessment, sports motion analysis, and multi-view 3D human pose estimation.

Education

• Ph.D. in Engineering, University of Tsukuba, Japan, April 2022 – March 2025
  International Master’s/Doctoral Program in Intelligent and Mechanical Interaction Systems (IMIS).
  Dissertation: A Study on Classifying Gait Impairment through Spatiotemporal Analysis of Video.
  Supervisor: Prof. Yoshihiro Kuroda.
• M.Eng. in Engineering, Kanazawa University, Japan, April 2020 – March 2022.
• B.Eng. in Computer Science and Technology, Inner Mongolia University of Technology, China, September 2014 – June 2018.

Research Interests

Computer vision; human motion analysis; video understanding; multi-view 3D human pose estimation; medical image and video analysis; deep learning for sports and healthcare.

Publications

Peer-reviewed journal articles and conference papers are listed below, most recent first within each category. The list is generated from the site’s publication records; my name is emphasized in the citations.

Journal Articles

1. Chen K, Asada T, Miura K, Kadone H, Kikuchi N, Homma Y, Yamazaki M, Kitahara I, Kuroda Y. Clinical knowledge-guided attention for gait-based adult spinal deformity assessment from monocular video. Journal of Medical Imaging, 13(5), 054002 (2026). doi:10.1117/1.JMI.13.5.054002. [Paper] [Details]
2. Wang YC, Chen K, Ienaga N, Kuroda Y. Incision trajectory tracing for electrosurgical navigation by CNN-based knife contacting frames extraction method. Computer Methods and Programs in Biomedicine, 286, 109612 (2026). doi:10.1016/j.cmpb.2026.109612. [Paper] [Details]
3. Chen K, Xu J, Asada T, et al. PhaseMix: A Periodic Motion Fusion Method for Adult Spinal Deformity Classification[J]. IEEE Access, 2024. [Paper] [Details]
4. Chen K, Asada T, Ienaga N, et al. Two-stage video-based convolutional neural networks for adult spinal deformity classification[J]. Frontiers in Neuroscience, 2023, 17: 1278584. [Paper] [Details]
5. Chen K X, Yamane S. Enhanced Full Attention Generative Adversarial Networks[J]. IEICE TRANSACTIONS on Information and Systems, 2023, 106(5): 813-817. [Paper] [Details]

Conference Papers

1. Chen K, Luo N, Kimura T, Naito E, Xie C, Kitahara I. CanonFuse3D: Human-Centric Canonical Fusion for Calibration-Free Dual-View 3D Pose Estimation in Skiing Videos. MMSports 2026, ACM Multimedia Workshop (accepted, 2026). (Accepted; forthcoming.) [Paper] [Details]
2. Chen K, Asada T, Miura K, Kadone H, Sakashita K, Sunami T, Yamazaki M, Kuroda Y, Kitahara I. A clinical knowledge-guided attention framework for gait-based adult spinal deformity diagnosis. Proceedings of SPIE, vol. 13925, pp. 256–261 (2026). doi:10.1117/12.3083822. [Paper] [Details]
3. K. Chen et al., "FilterNet: A Filtered Gait Motion Fusion Network for Classifying Adult Spinal Deformity," 2026 International Conference on Electronics, Information, and Communication (ICEIC), Macau, China, 2026, pp. 1-6, doi: 10.1109/ICEIC69189.2026.11386438. [Paper] [Details]
4. Xu J, Chen K, Kuroda Y, et al. Feel What You See: A Novel Sensory Interface Linking Visual Heat Cues and Instant Thermal Feedback[M]//Proceedings of the SIGGRAPH Asia 2025 Emerging Technologies. 2025: 1-2. [Paper] [Details]
5. Tsukumo T, Chen K, Asada T, et al. Spinal Disease Classification Using Deep Learning on Dual-View Videos[C]//Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2025, 2025: 1-5. [Paper] [Details]

For the latest bibliographic information, see my Google Scholar profile.

Research Support

• FY2025 — Young Researcher Support Program (First Call), Cybermedicine Research Center, University of Tsukuba. Research support awarded: ¥1,000,000.

Selected Research Projects

• Interpretable clinical gait analysis: Video-based adult spinal deformity assessment using phase-aware motion representations and clinical knowledge-guided attention, including PhaseMix, FilterNet, and related clinical gait studies.
• 3D human motion analysis for outdoor skiing: Human-centric canonicalization and calibration-free dual-view 3D pose fusion from moving-camera video (CanonFuse3D, MMSports 2026, accepted).
• Compensatory head motion during driving: Multi-view analysis of head movement and visual-scanning behavior under simulated visual-field limitations (ongoing).
• Image-guided electrosurgical navigation: Collaborative work on CNN-based identification of instrument–tissue contact frames for tracing incision trajectories.

Industry Experience

• Artificial Intelligence Intern, GHELIA Inc., Japan — June 2023 – April 2025. Computer vision and pattern recognition research and development.
• Artificial Intelligence Intern, RICOH Company, Ltd., Japan — February – April 2024. Applied computer vision and machine learning.
• Artificial Intelligence Intern, Toshiba Corporation, Japan — August – October 2023. AI and computer vision prototyping with Python and PyTorch.
• Web Application Engineer Intern, Rakuten Group, Inc., Japan — July – August 2023. Web application development.
• Java Software Engineer, Accenture (Shanghai), China — July 2018 – April 2019. Enterprise software development.

Technical Skills

• Programming: Python, Java.
• Deep learning and data science: PyTorch, PyTorch Lightning, Hugging Face, scikit-learn, pandas.
• Computer vision: Spatiotemporal video models, 2D/3D human pose estimation, multi-view geometry, and motion analysis.
• Tools and computing: Git, Docker, Linux.

Languages

• Chinese (Mandarin): Native.
• English: Fluent.
• Japanese: Advanced.