STM Journals Peer-reviewed research from Consortium e-Learning Network

Journal of Advancements in Robotics Review Article

A Next-Generation IoT-Enabled Smart Cane for the Visually Impaired: Integration of Advanced Navigation, Context-Aware Obstacle Detection, and Real-Time Voice Guidance

  1. Alakesh Hazra Department of Electronics and Communication Engineering, Greater Kolkata College of Engineering and Management, 24 Paragana
  2. Gopal Mondal Department of Electronics and Communication Engineering, Greater Kolkata College of Engineering and Management, 24 Paragana
  3. Jhuma Pal Department of Electronics and Communication Engineering, Greater Kolkata College of Engineering and Management, 24 Paragana
  4. Gour Gopal Jana Department of Electronics and Communication Engineering, Greater Kolkata College of Engineering and Management, 24 Paragana
  5. Niratyay Biswas Department of Electronics and Communication Engineering, Greater Kolkata College of Engineering and Management, 24 Paragana
  6. Trilochan Patra Department of Electronics and Communication Engineering, Greater Kolkata College of Engineering and Management, 24 Paragana

Abstract

The rapid growth of Internet of Things (IoT) technologies, combined with advances in embedded systems and artificial intelligence, has opened new possibilities for developing assistive mobility solutions tailored to the needs of individuals with visual impairments. This paper introduces an IoT- enabled smart cane designed to enhance independent mobility through intelligent environmental interpretation and context-aware navigation. Unlike traditional canes that rely solely on tactile feedback, the proposed system incorporates multiple sensing modules, including ultrasonic, infrared, and ambient light sensors, to detect obstacles, surface variations, and low-visibility conditions. These sensors work in tandem with GPS and cloud-based positioning services to deliver precise, real-time guidance in both indoor and outdoor environments. A key component of the smart cane is its voice- based feedback mechanism, which relays navigation cues, hazard alerts, and location information through an adaptive speech interface that adjusts to ambient noise levels. Multi-sensor data fusion algorithms ensure reliable and accurate interpretation of complex surroundings, minimizing false alarms while improving user confidence. Additionally, optional cloud connectivity supports long-term data analytics and hazard mapping, offering insight into frequently encountered obstacles and contributing to more inclusive urban planning efforts. Overall, this research highlights a comprehensive assistive tool that not only enhances personal navigation but also fosters accessibility, safety, and autonomy for visually impaired individuals in modern smart cities.

Keywords

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