AIoT-Based Smart Stick for Visually Impaired Person

Jivrajani, Krupal; Patel, Shobhit K.; Parmar, Chandrasinh; Surve, Jaymit; Ahmed, Kawsar; Bui, Francis M.; Al-Zahrani, Fahad Ahmed

doi:10.1109/tim.2022.3227988

Cited by 17 publications

(7 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…A smart stick with obstacle detection, object recognition, health monitoring, and navigation features to aid the visually impaired [5]. The smart stick can greatly improve the quality of life for visually impaired, making it easier as well as cost-effective and user-friendly.…”

Section: Literature Surveymentioning

confidence: 99%

Integrated Intelligent Assistant for Visually Impaired

2024

Preprint

View full text Add to dashboard Cite

A person with a visual impairment finds it more difficult to recognize objects, avoid obstacles, and navigate independently. Visual impairment makes more difficult for those people to learn about their surroundings. Navigating through spaces becomes intricate, as the individual must rely on other senses and aids like canes or guide dogs to detect obstacles and safely maneuver through surroundings. Recognizing objects becomes a complex endeavor, as visual cues are limited or absent, leading to difficulties in identifying items, faces, or even familiar places. The proposed work introduces a brand-new visual assistance solution by creating a single, intelligent device that empowers visually impaired users through its multifaceted functionalities obtained by combining deep learning and machine learning algorithms. Image processing techniques such You Only Look Once (YOLO), Haar-Cascade algorithm is used for object detection and face recognition with the help of camera and other sensors. A comprehensive case study was conducted, demonstrating the real-time capabilities of YOLOv8 in detecting objects and obstacles on public roads, offering empirical evidence of its effectiveness in assisting visually impaired individuals in navigating urban environments safely and independently. The suggested prototype, in comparison to the white cane, provides the visually impaired with increased accessibility, comfort, and ease of navigation, according to the results.

show abstract

Section: Literature Surveymentioning

confidence: 99%

Integrated Intelligent Assistant for Visually Impaired

2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Often, they must rely on the assistance of others, and individuals who are blind require the support of a cane for mobility. To address these difficulties, the solution lies in the integration of Artificial Intelligence of Things (IoT) to design smart devices specifically tailored for the visually impaired [1].…”

Section: Literature Reviewmentioning

confidence: 99%

IoT-Based Smart Stick for Visually Impaired People

Usman,

Shakeel,

Atif

et al. 2023

JAIC

View full text Add to dashboard Cite

This thesis presents the development of a smart stick aimed at enhancing the mobility, safety, and independence of visually impaired individuals. The objective of the study was to integrate various features into a cohesive and user-centric device. The stick incorporates GSM/GPS tracking for continuous user location monitoring, a smart home automation mechanism that allows the user to turn utilities ON/OFF with a single button press, obstacle detection using ultrasonic sensors, battery optimization techniques including solar charging and voltage supply to the battery by use of a stepper motor, emergency communication through GSM technology, and stick tracking within a certain radius using RF modules. The findings of the study demonstrate the successful integration and functionality of the proposed features, cover the gaps identified with the help of extensive research and critical literature review; and extend a gateway into the ocean of future research and diverse feature employment that could be incorporated in the smart stick.

show abstract

“…However, the smart cane system could only recognize faces at a limited range, and the number of faces that could be recognized was also very limited. K Jivrajani et al (2022) [17] proposed an AIoT-based smart cane, which used Raspberry PI 3 as the main control module and OmniVision OV5647 as the camera module. The system could identify some daily necessities, but the system lacked the ability to identify obstacles on the road.…”

Section: Introductionmentioning

confidence: 99%

A Smart Cane Based on 2D LiDAR and RGB-D Camera Sensor-Realizing Navigation and Obstacle Recognition

Mai,

Chen,

Zeng

et al. 2024

Sensors

View full text Add to dashboard Cite

In this paper, an intelligent blind guide system based on 2D LiDAR and RGB-D camera sensing is proposed, and the system is mounted on a smart cane. The intelligent guide system relies on 2D LiDAR, an RGB-D camera, IMU, GPS, Jetson nano B01, STM32, and other hardware. The main advantage of the intelligent guide system proposed by us is that the distance between the smart cane and obstacles can be measured by 2D LiDAR based on the cartographer algorithm, thus achieving simultaneous localization and mapping (SLAM). At the same time, through the improved YOLOv5 algorithm, pedestrians, vehicles, pedestrian crosswalks, traffic lights, warning posts, stone piers, tactile paving, and other objects in front of the visually impaired can be quickly and effectively identified. Laser SLAM and improved YOLOv5 obstacle identification tests were carried out inside a teaching building on the campus of Hainan Normal University and on a pedestrian crossing on Longkun South Road in Haikou City, Hainan Province. The results show that the intelligent guide system developed by us can drive the omnidirectional wheels at the bottom of the smart cane and provide the smart cane with a self-leading blind guide function, like a “guide dog”, which can effectively guide the visually impaired to avoid obstacles and reach their predetermined destination, and can quickly and effectively identify the obstacles on the way out. The mapping and positioning accuracy of the system’s laser SLAM is 1 m ± 7 cm, and the laser SLAM speed of this system is 25~31 FPS, which can realize the short-distance obstacle avoidance and navigation function both in indoor and outdoor environments. The improved YOLOv5 helps to identify 86 types of objects. The recognition rates for pedestrian crosswalks and for vehicles are 84.6% and 71.8%, respectively; the overall recognition rate for 86 types of objects is 61.2%, and the obstacle recognition rate of the intelligent guide system is 25–26 FPS.

show abstract

AIoT-Based Smart Stick for Visually Impaired Person

Cited by 17 publications

References 30 publications

Integrated Intelligent Assistant for Visually Impaired

Integrated Intelligent Assistant for Visually Impaired

IoT-Based Smart Stick for Visually Impaired People

A Smart Cane Based on 2D LiDAR and RGB-D Camera Sensor-Realizing Navigation and Obstacle Recognition

Contact Info

Product

Resources

About