Analysis and Modeling of Automated Walking Guide to Enhance the Mobility of Visually Impaired People

Abstract

The development of walking guides has become a prominent research due to the rapid growth of visually impaired people in recent decades. Although numerous systems have been developed to aid the visually impaired people, a considerable portion of these are limited in their scopes. This thesis has implemented a spectacle prototype to assist these individuals with safe and efficient walking in the surrounding’s environment. The spectacle prototype is modeled in SolidWorks (3D model) considering the dimension of each electronic components. In the modeling, the front ultrasonic sensor is positioned in the spectacle to detect the front obstacles only, the left and right ultrasonic sensors are set to 45 degree from the spectacle center point in order to detect obstacles within the shoulder and arm of user; another ultrasonic sensor is positioned towards the ground facing for the detection of pothole. The Rpi camera is positioned at the center point of the spectacle. In addition, the right and left temple of the spectacle is designed to position the raspberry pi and battery respectively. The usage of spectacle based walking guide would help the visually impaired people to scan the surroundings. Three pieces of distance measurement sensors (ultrasonic sensor) is used in the walking guide in order to detect the obstacle in each direction including front, left and right. In addition, the system detects the potholes on the road surface using sensor and convolutional neural network (CNN). Overall, the spectacle prototype consists of four ultrasonic sensors; raspberry pi, Rpi camera and battery. CNN technique, runs on raspberry pi, is used to detect the pothole on the road surface. The pothole images are trained initially using convolutional neural network in a host computer and the potholes are detected by capturing a single image each time. The experimental study demonstrates that 98.73% accuracy is achieved by the front sensor with an error rate of 1.26% when the obstacle is at 50 cm distance. In addition, the results reveal that the system obtains the highest accuracy, precision and recall 92.67%, 92.33% and 93% respectively for potholes detection. The electronic spectacle gives a direct audio signal to the user via headphone for avoiding hindrances effectively.