智慧醫療系統與法規科學實驗室
Research Field
Dr. Hsin-Hung Kuo is an Assistant Professor in the Department of Biomedical Engineering at Chung Yuan Christian University, Taiwan. His research interests encompass Artificial Intelligence (AI), Medical Image Analysis, Biomedical Signal Processing, Smart Healthcare, Digital Health, Rehabilitation Engineering, and Wearable Biomedical Sensing Technologies.
His recent research focuses on integrating deep learning, computer vision, human pose estimation, and physiological signal analysis to develop non-contact intelligent health monitoring systems. These technologies are applied to pediatric developmental assessment, healthy aging, rehabilitation medicine, and smart healthcare. His research topics include AI-based posture recognition, physiological signal analysis, digital biomarkers, and the development of intelligent healthcare platforms.
Dr. Kuo actively collaborates with hospitals, rehabilitation centers, and special education institutions to advance interdisciplinary research on the application of artificial intelligence in clinical medicine, child development, rehabilitation assessment, and healthcare. He is committed to translating innovative research outcomes into intelligent healthcare technologies with practical clinical value.
The laboratory welcomes students and researchers who are interested in artificial intelligence, medical informatics, biomedical engineering, medical image analysis, intelligent sensing technologies, digital health, and interdisciplinary healthcare research. Together, we strive to develop innovative research with both academic impact and meaningful clinical applications.
Lab's Introduction
The Intelligent Medical Systems and Regulatory Science(IMSRS Lab) is dedicated to advancing interdisciplinary research at the intersection of artificial intelligence, biomedical engineering, and healthcare. Our mission is to develop innovative technologies that improve health assessment, disease prevention, rehabilitation, and personalized healthcare through intelligent sensing, computer vision, and data-driven approaches.
Our research integrates artificial intelligence, deep learning, computer vision, biomedical signal processing, wearable sensing technologies, and digital health to develop next-generation healthcare solutions. Current research topics include AI-based posture recognition, physiological signal analysis, digital biomarkers, non-contact health monitoring systems, medical image analysis, rehabilitation engineering, and intelligent healthcare platforms.
The laboratory actively collaborates with hospitals, rehabilitation centers, educational institutions, and industrial partners to translate research findings into practical clinical and healthcare applications. We are particularly interested in developing AI-assisted technologies for pediatric healthcare, rehabilitation medicine, healthy aging, and smart healthcare systems.
Our laboratory provides students with opportunities to participate in interdisciplinary research projects, publish in international journals and conferences, and collaborate with clinicians and researchers from diverse fields. We welcome motivated undergraduate students, graduate students, and research assistants who are passionate about artificial intelligence, biomedical engineering, digital health, and smart healthcare technologies to join our team and contribute to impactful research.
Research Topics
Our laboratory focuses on interdisciplinary research in artificial intelligence, biomedical engineering, and digital healthcare. Current research topics include:
Artificial Intelligence (AI) for Healthcare
Machine Learning and Deep Learning
Computer Vision and Human Pose Estimation
Medical Image Analysis
Biomedical Signal Processing
Digital Biomarkers and Digital Health
Wearable and Non-contact Health Monitoring Systems
Intelligent Sensing Technologies
Smart Healthcare and Internet of Medical Things (IoMT)
Rehabilitation Engineering and Assistive Technologies
Pediatric Healthcare and Developmental Assessment
AI-assisted Rehabilitation and Clinical Decision Support
Human Activity Recognition and Behavior Analysis
1. 2026年指導學生參加第二十四屆離島資訊技術與應用研討會
指導學生發表論文「適用於離島長照之物聯網智能拐杖跌倒分級通報系統」，結合物聯網感測、跌倒偵測與智慧通報技術，提升離島及偏鄉高齡照護之安全性與即時性，展現智慧照護科技之創新應用成果。
2. 2026年3月受邀擔任專題演講講者
應銘傳大學資訊工程學系邀請，於該系論文研討會進行專題演講，講題為「智慧醫療系統創新整合與跨領域應用經驗分享」，分享人工智慧、物聯網及醫療資訊技術於智慧醫療系統之研究成果與實務經驗，促進跨領域學術交流。
3. 2026年指導國際學生參加第二十一屆數位訊號處理創思設計競賽
指導國際學生團隊參賽，榮獲國際組佳作，展現學生於數位訊號處理、人工智慧及系統整合領域之研發能力，並提升國際學生之研究與創新實作成果。
4. 2025年指導學生榮獲全國資訊教育與科技應用專題競賽佳作
指導學生以「基於物聯網邊緣運算與規則推理的環境舒適度判斷方法」為題參賽並獲得佳作，研究結合邊緣運算、環境感測與智慧決策技術，展現物聯網與人工智慧於智慧環境監測之應用價值。
5. 2025年指導學生完成研究論文並獲期刊接受刊登
指導學生共同完成「結合太陽能供電與人工智慧分析的智慧灌溉物聯網系統」研究，成功投稿資訊電子學刊並獲接受發表。研究整合再生能源、物聯網及人工智慧分析技術，具備智慧農業應用與永續發展之實務價值，並展現學生研究成果轉化為學術產出的能力。
PhD in Biomedical Engineering from the Catholic University of America
Job Description
The intern will participate in AI-based biomedical engineering research, focusing on biomedical signal processing, computer vision, wearable sensing, and smart healthcare applications. The project involves developing machine learning models, analyzing physiological signals (e.g., PPG, HRV), implementing AI-based monitoring systems, and evaluating healthcare applications. Students will gain experience in AI algorithm development, experimental design, data analysis, and international research collaboration.
Preferred Intern Educational Level
Preferred Intern Educational Level
☑ Undergraduate students (Junior/Senior level)
☑ Master's students
☑ Ph.D. students
Description:
Applicants who are currently pursuing undergraduate, master's, or doctoral degrees in Biomedical Engineering, Computer Science, Electrical Engineering, Artificial Intelligence, Data Science, or related fields are preferred. Senior undergraduate students with strong programming skills and research interests are especially encouraged to apply.
Skill sets or Qualities
Applicants are encouraged to have experience in one or more of the following areas:
- Artificial Intelligence
- Machine Learning
- Deep Learning
- Biomedical Engineering
- Computer Science
- Electrical Engineering
- Medical Engineering
- Computer Vision
- Python programming
- OpenCV
- PyTorch
- TensorFlow
- MATLAB
Students with strong academic motivation but limited research experience are also encouraged to apply.
Job Description
Possible research tasks include:
- Developing computer vision algorithms using deep learning techniques
- Applying pose estimation frameworks, such as MediaPipe and RTMPose, for human movement analysis
- Designing AI models for posture recognition, activity monitoring, and rehabilitation assessment
- Processing and analyzing image/video-based healthcare data
- Implementing machine learning and deep learning models using Python-based frameworks
- Conducting experimental validation and performance evaluation of AI systems
- Supporting research documentation, literature review, and academic presentations
Preferred Intern Educational Level
Students majoring in Computer Science, Artificial Intelligence, Biomedical Engineering, Electrical Engineering, Data Science, or related fields are encouraged to apply. Experience with Python, OpenCV, PyTorch/TensorFlow, machine learning, or computer vision is preferred.
Skill sets or Qualities
Applicants should possess the following skills and qualities:
- Strong interest in Artificial Intelligence (AI), Computer Vision, and Smart Healthcare Technologies
- Basic knowledge of machine learning and deep learning algorithms
- Programming experience in Python is preferred
- Familiarity with AI frameworks such as PyTorch, TensorFlow, or OpenCV is an advantage
- Experience with image/video processing, human pose estimation, or biomedical data analysis is preferred
- Basic understanding of data analysis, experimental design, and research methodology
- Ability to learn new technologies and solve technical problems independently
- Good communication skills and willingness to collaborate in an interdisciplinary research environment
- Ability to work effectively with international researchers and graduate students
- Proactive attitude, curiosity, and strong motivation for academic research