Mobility Support with Intelligent Obstacle Detection for Enhanced Safety
Year: 2024
Type: article
Abstract: In recent years, assistive technology usage among the visually impaired has risen significantly worldwide. While traditional aids like guide dogs and white canes have limitations, recent innovations like RFID-based indoor navigation systems and alternative sensory solutions show promise. Nevertheless, there is a need for a user-friendly, comprehensive system to address spatial orientation challenges for the visually impaired. This research addresses the significance of developing a deep learning-based walking assistance device for visually impaired individuals to enhance their safety during mobility. The proposed system utilizes real-time ultrasonic sensors attached to a cane to detect obstacles, thus reducing collision risks. It further offers real-time recognition and analysis of diverse obstacles, providing immediate feedback to the user. A camera distinguishes obstacle types and conveys relevant information through voice assistance. The system’s efficacy was confirmed with a 90–98% object recognition rate in tests involving various obstacles. This research holds importance in providing safe mobility, promoting independence, leveraging modern technology, and fostering social inclusion for visually impaired individuals.
Source: Optics
DOI: 10.3390/opt5040032
Institution: Kangwon National University
Language: English
Subfield: Occupational Therapy
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $1,364
OpenAlex ID: W4403729711
