ROS-Based Smart Walker with Fuzzy Posture Judgement and Power Assistance.

Area of Science:

• Robotics and Assistive Technology
• Gerontology and Biomedical Engineering

Background:

• The aging global population faces challenges like reduced mobility and increased fall risk.
• Assistive devices are crucial for maintaining independence and quality of life in the elderly.

Purpose of the Study:

• To design and develop a smart walker system to assist elderly individuals with independent walking.
• To enhance walking safety, stability, and user experience through intelligent control and monitoring.

Main Methods:

• A smart walker prototype was developed using a distributed Robot Operating System (ROS) framework.
• A control kernel combining Raspberry Pi and PIC microcontroller was implemented.
• Sensors were integrated to collect environmental data (road slope, walker velocity) and user biometrics (grip forces).
• A fuzzy controller was employed to generate assistive forces based on real-time sensor data.

Main Results:

• The fuzzy controller effectively produced assistive forces, promoting smoother and safer walker movement.
• The integrated sensors provided crucial data for adaptive control and user monitoring.
• A companion mobile application was developed for remote monitoring and location tracking by guardians.

Conclusions:

• The fuzzy controller-based smart walker, integrated with ROS, offers a promising solution for enhancing elderly mobility and safety.
• The system demonstrates the potential to reduce fall-related injuries and support independent living for seniors.
• Future iterations can further refine control algorithms and sensor integration for enhanced performance.