The Haptic Feedback Cane is a cutting-edge assistive device designed to improve the navigation and mobility of individuals with visual impairments. This project represents an innovative convergence of various technologies, including ultrasonic sensors, advanced imaging systems, and haptic feedback mechanisms. By integrating these elements, the cane offers a unique solution for obstacle detection and navigation assistance.
Utilizes computer vision algorithms, primarily focusing on edge detection, to identify and classify obstacles. Cameras integrated into the cane provide real-time visual data, which is processed to detect edges, shapes, and specific features like crosswalks and curbs. The system is designed to operate effectively in different lighting conditions, enhancing its reliability and usability.
Ultrasonic sensors are employed to detect objects in the cane's path, providing an additional layer of spatial awareness. These sensors help in determining the distance to and size of obstacles, contributing to a more comprehensive understanding of the surroundings.
The cane translates visual and ultrasonic data into tactile feedback through integrated vibration motors. This haptic feedback informs the user about the proximity and nature of obstacles, enhancing spatial awareness and decision-making in real-time.
Ergonomics and user comfort are central to the cane's design, ensuring that it is lightweight, easy to handle, and intuitive to use. The device's hardware and software components are optimized for low power consumption, ensuring longer battery life. A user-friendly interface allows for easy customization and adjustment of settings based on individual preferences and needs.
Aims to significantly enhance the autonomy and safety of visually impaired individuals. The integration of multiple sensing technologies sets a new standard in the field of assistive devices. Potential applications extend beyond individual use, contributing to research and development in related areas such as robotics and autonomous navigation systems.
Ongoing research and development are focused on improving accuracy, reducing false positives, and enhancing the cane's adaptability to various environments. Exploration of AI and machine learning algorithms to further refine obstacle detection and classification capabilities.
Special thanks to all team members, mentors, and contributors who have supported and guided this project.