A Cane-Mounted System for Dynamic Orientation Prediction for Correcting Incorrect Cane-Tapping by Visually Challenged Persons

People with visual impairments rely on Electronic Travel Aids (ETAs), such as sensor-equipped guide canes, for safe and effective navigation. Misalignment or improper handling of these devices can reduce their effectiveness, increasing the risk of collisions and injuries. This paper presents an AIbased embedded system designed to predict and correct the orientation of a guide cane in real time. By integrating an Inertial Measurement Unit (IMU) with a neural network, the system continuously monitors the cane's lateral angle and orientation while providing feedback to help the user self-correct. The feedback is proportional to the degree of error, guiding users to maintain proper cane positioning during mobility. The device logs data that can be visualized remotely, offering mobility trainers valuable insights into the user's navigation patterns. Evaluation by visually impaired users demonstrated that the system effectively aided in real-time orientation correction. This effort contributes towards safe use of ETAs for navigation by visually challenged persons.