Welcome to

• Physical AI and Nursing Robotics:

Explore how artificial intelligence can move beyond perception and decision-making to act safely and effectively in the physical care environment. Our research focuses on translating nursing expertise, motion data, and embodied intelligence into methods that support the future development of nursing robots.

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• AI-Enabled Nursing Documentation:

​Develop intelligent nursing documentation systems that transform nurses’ clinical speech, language, and workflow narratives into standardized, structured, and reusable data. 

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• Health Agents for Chronic Disease Management:

​Design intelligent health agents for cardiovascular chronic disease management, especially for exercise habit formation among individuals with hypertension and high-normal blood pressure. By integrating behavioral science, dual-process theory, and reinforcement learning, we aim to create adaptive interventions that respond to individual needs over time.

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• Human Cognition, Behavior, and Neural Mechanisms:

Investigate the cognitive, neural, and behavioral mechanisms underlying health behavior change and nursing-related motor tasks. Using EEG, fNIRS, eye-tracking, and behavioral data, we explore how individuals perceive, decide, move, and interact in health and care scenarios.

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• ​Nursing Large Action Model:

Build high-quality motion datasets from expert nursing maneuvers, including patient turning, CPR, and the Heimlich maneuver. Through motion capture and biomechanical analysis, we aim to develop a Nursing Large Action Model, or Nurse-LAM, that enables AI systems to understand, learn, and reproduce professional nursing actions.

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• Human–AI Collaboration in Clinical Nursing:

Examine how nurses, patients, intelligent agents, and physical AI systems can collaborate in clinical and educational settings. We aim to develop trustworthy, interpretable, and clinically meaningful AI systems that enhance nursing practice rather than replace human expertise.​​