A Review on Implementation of a Multi-Functional Vision Assistant for the Visually Impaired

Abstract

The development of intelligent assistive devices is crucial for enhancing the mobility and independence of visually impaired individuals. This review synthesizes recent advancements in electronic travel aids, particularly smart walking sticks and wearable systems, that leverage sensor fusion, microcontrollers, and artificial intelligence. Early systems primarily utilized ultrasonic and moisture sensors with Arduino microcontrollers to provide basic obstacle and water detection through audio and vibrational feedback. Recent research has significantly evolved to incorporate sophisticated technologies such as Raspberry Pi, computer vision, and deep learning. The integration of object detection algorithms like YOLO (You Only Look Once) and Convolutional Neural Networks (CNNs) enables real-time identification and classification of a wide range of obstacles, including people and vehicles. Furthermore, studies explore the use of IoT for emergency communication via GSM/GPS, deep reinforcement learning for autonomous navigation, and 3D point cloud modeling for dynamic path planning in indoor environments. The consistent findings across these studies highlight a paradigm shift from simple hazard detection to providing descriptive environmental awareness. Key challenges remain in optimizing the trade-off between detection accuracy and real-time performance on embedded devices. The future trajectory of this field points towards the increased use of model compression techniques, multi-sensor data fusion, and machine learning to create more affordable, robust, and intuitive navigation systems that significantly improve the safety and autonomy of visually impaired users.

Introduction

Advances in embedded systems, artificial intelligence, and the Internet of Things (IoT) have significantly transformed assistive technologies for visually impaired individuals. While traditional aids like the white cane provide basic mobility support, modern Electronic Travel Aids (ETAs) enhance safety and independence through intelligent sensing, object recognition, and real-time feedback. Early systems relied on Arduino-based ultrasonic sensors for basic obstacle detection, later expanding to include hazard detection for water, fire, and stairs. The introduction of powerful platforms such as Raspberry Pi and ESP32, combined with AI techniques, marked a major shift toward smart navigation aids.

Recent research increasingly integrates computer vision and deep learning, particularly CNNs and YOLO-based object detection, enabling real-time identification and classification of environmental objects with audio and vibration feedback. IoT technologies further enhance safety through GPS-based tracking, GSM emergency communication, mobile applications, and cloud connectivity. Multi-sensor fusion approaches combine ultrasonic, water, smoke, light, temperature, and motion sensors to provide comprehensive environmental awareness.

The literature demonstrates steady progress from low-cost sensor-based sticks to AI-powered, multi-modal assistive systems capable of indoor and outdoor navigation, hazard detection, and even social interaction via facial recognition. Advanced models employ lightweight and optimized deep learning architectures suitable for embedded deployment, achieving high accuracy and real-time performance. Emerging approaches also explore reinforcement learning, SLAM, wearable devices such as smart shoes, and smartphone integration.

Conclusion

The development of smart assistive devices for the visually impaired has progressed from basic sensor-based systems to sophisticated AI-enhanced platforms. Foundational work demonstrated the effective use of ultrasonic sensors and Arduino microcontrollers for reliable obstacle and hazard detection [1], [8]. The field has since evolved to incorporate multi-sensor data fusion [2], [16], [19], IoT connectivity for safety and tracking [4], [13], [16], and powerful deep learning models for real-time object recognition and classification [3], [4], [15]. These models, including YOLO and CNNs, have transformed assistive devices from simple warning systems into descriptive navigation aids. However, key challenges persist, particularly in achieving real-time performance with complex AI models on resource-constrained hardware [15], [17] and the need for more comprehensive environmental understanding that includes dynamic obstacles and complex terrains [9], [18]. Addressing these limitations requires a focused effort on model optimization through pruning and quantization [15], the development of robust benchmarking frameworks [18], and the exploration of advanced navigation techniques like deep reinforcement learning [17] and visual SLAM [17]. Future research directions should also prioritize the integration of cross-modal applications, such as facial recognition for social interaction [13] and 3D point cloud modeling for indoor path planning [9]. With continued innovation in these areas, the goal of creating fully autonomous, intuitive, and universally accessible navigation systems that grant visually impaired individuals unprecedented independence is steadily becoming an achievable reality.

References

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Copyright

Copyright © 2026 Roshan Roy K, Thanmay K Babu, Midhun E S, Arun A A, Gadha M M. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.