Modular Smart Glasses for Real-Time Obstacle Detection in Blind Navigation
Abstract
This paper describes an innovative assistive device that attaches to eyeglasses to aid the mobility of blind individuals. The device employs infrared or ultrasonic sensors to detect environmental obstacles, converting this information into tactile and auditory feedback, providing users with a broader and more accurate spatial awareness. The collected data is processed and translated into tactile signals (vibration) or auditory signals (via sound transducers). The interval of these signals varies based on the distance from obstacles, enabling users to make quick and safe decisions in real time (empirical tests indicated an average detection accuracy above 92% and a latency of approximately 120–150 ms between detection and feedback). Currently, the most common mobility aids for visually impaired individuals are white canes and guide dogs, which have significant limitations. Canes are restricted to detecting obstacles from waist level downward and have a short range, while guide dogs are expensive and require extensive training. Hence, developing a portable and discreet technology capable of detecting obstacles beyond the reach of traditional canes is essential to improve the independence and safety of these individuals. The device was designed to be lightweight, comfortable, and aesthetically discreet, without compromising the user’s autonomy or quality of life. Financial accessibility is also prioritized, with efforts to utilize low-cost materials and technologies to enable large-scale production, benefiting as many individuals as possible. By addressing a recurrent challenge with a practical solution, this project offers a novel approach to mobility, autonomy, and inclusion for visually impaired individuals, expanding their ability to interact with their surroundings.
Fields of Study & Subjects
Tactile and Sensory Interactions
Gaze Tracking and Assistive Technology
Advanced Sensor and Energy Harvesting Materials
Publication details
- Journal / source
- IEEE Internet of Things Magazine
- Publisher
- Institute of Electrical and Electronics Engineers (IEEE)
- Volume
- 9
- Issue
- 4
- Pages
- 157–164
- Publication date
- 12 January 2026