Design Methodology for a Backrest-Lifting Nursing Bed Based on Dual-Channel Behavior-Emotion Data Fusion and
Xiaochan Wang1, Cheolhee Cho1, Peng Zhang2
1Department of Design, Kyungpook National University, Daegu 41566, Republic of Korea.
Biomimetics (Basel, Switzerland)
|November 26, 2025
Summary
This study introduces a sustainable, AI-driven nursing bed design optimizing mobility assistance. It enhances user comfort and safety by integrating user needs and biomechanical data for improved rehabilitation outcomes.
Area of Science:
- Rehabilitation Engineering
- Human-Computer Interaction
- Sustainable Design
Background:
- Growing elderly populations and rehabilitation needs necessitate advanced assistive devices.
- Current nursing beds lack adaptability, safety, and emotional consideration.
- Need for human-centered, sustainable solutions in assistive technology.
Purpose of the Study:
- To develop a human-centered, data-driven optimization pipeline for an intelligent nursing bed.
- To integrate user behavior, emotional needs, and biomechanical data for enhanced design.
- To apply GreenAI principles for energy efficiency and environmental responsibility.
Main Methods:
- Dual-channel data fusion combining movement and emotional data (TF-IDF, LDA).
- Fuzzy Kano model for prioritizing design objectives (e.g., joint protection, comfort).
- AnyBody-based biomechanical simulation to quantify joint and muscle loads.
Main Results:
- Optimized nursing bed design shows improved adaptability and functional reliability.
- Simulations confirmed reduced joint stress and enhanced user comfort.
- Successful integration of user demands, biomechanics, and sustainable practices.
Conclusions:
- The proposed framework offers a scalable paradigm for intelligent rehabilitation equipment.
- This sustainable methodology aligns rehabilitation goals with environmental responsibility.
- Potential for AI-driven adaptive control and clinical validation in future research.
