Development and Evaluation of a Tool for Blind Users Utilizing AI Object Detection and Haptic Feedback

This paper presents the development and evaluation of a smartphone application designed to improve accessibility for blind users. It uses the lightweight EfficientDet-lite2 model and the comprehensive COCO dataset in order to provide real-time object detection. The novelty of the application is in the integration of haptic feedback, which is activated when users touch objects that are detected on the screen, combined with audio notifications that announce the name of the detected object in multiple languages. This multimodal feedback mechanism helps blind users to recognize, explore, and move within their environment more effectively and safely. Extensive usability and user experience evaluation was conducted with blind and blindfolded users. The evaluation assessed the usability, effectiveness, accessibility, and user satisfaction and experience of the application. Additionally, a comparative analysis was performed between the use of haptic feedback and scenarios where haptic feedback was disabled. The results show a higher level of user satisfaction, greater ease of use, and significant potential for improving the independence of blind people when the haptic feedback is enabled. The findings also suggest that the inclusion of haptic feedback significantly enhances the user experience. This study underlines the importance of multimodal feedback systems in assistive technologies and the potential of mobile applications to provide accessible solutions for blind users.