Smart Assistive System for Paralysis People

Mobility impairments due to paralysis present significant challenges, limiting independence and daily activities. This research focuses on developing an Electrooculography (EOG)-Based Blink-Controlled Wheelchair, enabling hands-free movement using eye blinks as the primary input. The system utilizes the Bio Amp EXG Pill to capture EOG signals, which are processed by an ESP32 microcontroller to detect voluntary eye blinks. A single blink triggers forward movement, while the absence of a blink stops the wheelchair. To enhance usability, an HTML-based web interface has been integrated, allowing users or caregivers to control the wheelchair in all directions remotely. The system employs an L298N motor driver to regulate two BO motors, ensuring efficient and reliable motion control. The proposed solution offers a cost-effective, power-efficient, and user-friendly alternative to traditional joystick or voice-controlled wheelchairs. The combination of blink-based control for autonomous movement and web-based remote control provides enhanced accessibility and flexibility. Future improvements may include machine learning algorithms for better blink detection, wireless connectivity for remote monitoring, and adaptive calibration for different users. This research contributes to the advancement of assistive mobility solutions, empowering individuals with paralysis to navigate their surroundings independently.