Development of a Microcontroller-Based Cane for the Visually Impaired

Abstract

This research aims to design and develop a microcontroller-based cane for visually impaired individuals to enhance safety and independence in daily life. The developed system integrates an ESP32 microcontroller with multiple sensors, including ultrasonic sensors for detecting obstacles at ground level and overhead, an MPU6050 inertial measurement unit for fall detection, and a GPS module for geographic positioning. A real-time sequential signal processing algorithm is implemented to provide alerts to users via audio signals and to transmit emergency status information to caregivers through the ThingsBoard cloud platform using Internet of Things (IoT) technology. The experimental results indicate that the system achieves high accuracy in obstacle detection within a range of 50–100 centimeters, with an average error of less than 1.00 centimeter. The fall detection system is capable of accurately identifying a fall at a tilt angle of 90 degrees with 100% accuracy. For the GPS positioning system, the average error ranges from 2.32 to 15.48 meters, which is considered acceptable for personal tracking applications. The empirical findings demonstrate that the developed smart cane is stable and effective in helping users avoid hazards in blind spots and critical situations.