Artificial Intelligence-Enabled Caregiving Walking Stick Powered by Ultra-Low-Frequency Human Motion

Area of Science:

• Biomedical Engineering
• Wearable Technology
• Internet of Things (IoT)

Background:

• The growing elderly and motion-impaired populations present significant societal challenges.
• Traditional walking sticks offer limited functionality beyond physical support.
• There is a need for advanced assistive devices for enhanced user monitoring and safety.

Purpose of the Study:

• To develop an intelligent walking stick as a healthcare-monitoring platform for motion-impaired individuals.
• To integrate energy harvesting and advanced sensing capabilities into a walking stick.
• To create a self-sustainable IoT system for real-time user well-being and activity monitoring.

Main Methods:

• Designed a linear-to-rotary structure for efficient energy harvesting from low-frequency motion.
• Integrated two self-powered triboelectric sensors for motion feature extraction.
• Utilized deep learning for advanced data analysis, including identity recognition and disability evaluation.
• Developed a self-sustainable IoT system with GPS tracing and environmental sensing.

Main Results:

• Achieved highly efficient energy harvesting from walking stick motion.
• Enabled accurate identity recognition, disability evaluation, and motion status distinguishing using deep learning.
• Demonstrated a self-sustainable IoT system with comprehensive monitoring functions.
• Validated the walking stick's potential as a caregiver in various usage scenarios.

Conclusions:

• The developed walking stick serves as an intelligent healthcare-monitoring platform.
• It enhances safety and autonomy for motion-impaired users through real-time monitoring.
• This technology has the potential to significantly improve the quality of life for the elderly and disabled.