IBGS: A Wearable Smart System to Assist Visually Challenged

Abstract

Traditional blind guide devices are large and high cost. In this paper, a kind of intelligent blind guide system (IBGS) is introduced. GD32 is used as the main control chip, it cooperates with various functional modules to realize traffic light recognition, obstacle avoidance, payment, and navigation functions on the basis of speech recognition. At the same time, IBGS uses WIFI instead of Bluetooth to get rid of the dependence on smart phones. In addition, a cloud database is built, and the Internet of Things technology is used to realize the information interconnection between the IBGS, the database, and the guardian's mobile terminal. In order to better realize the function of speech recognition, this paper proposes a Conv-Transformer Transducer (ConvT-T) speech recognition framework based on Weak-Attention Suppression (WAS), which improves the efficiency of multi-head attention through WAS. The proposed method achieves word error rate (WER) of 3.2% in test-clean and 7.9% in test-other on the LibriSpeech ASR corpus with only 73M parameters, which reduces the WER by 0.3% and 0.4% compared to ConvT-T respectively, indicating that WAS can effectively play the role of suppressing non-critical attention on the ConvT-T framework. At the same time, the IBGS has been tested in a comprehensive application scenario. In outdoor traffic light recognition, speech recognition, and obstacle avoidance tests, the accuracy rates of 92.33%, 90.33%, and 96.67% were achieved respectively. The results show that the IBGS can effectively deal with various challenges encountered by the blind in their daily outdoor walking.

An IoT-based vision alert for blind using interdisciplinary approaches

“…The buzzer will produce different sound that depend upon the distance of obstacle. K. Xia and his team members [5] incorporates a sound-absorbing cavity structure to improve speech interaction efficiency. IBGS features traffic light recognition using the YOLO framework and OpenCV, obstacle avoidance through laser ranging, and a mobile payment function with an NFC chip.…”

PerceptGuide: A Perception Driven Assistive Mobility Aid Based on Self-Attention and Multi-Scale Feature Fusion

“…RGB-D camera-based assistance [11] in indoor environments involves complex processing, leading to slower responses and requiring sophisticated hardware without environmental details. A cloud-based wearable assistive aid uses speech recognition for navigation [12], offering features like traffic light detection, obstacle avoidance, payment support, and navigation. Another aid relies on RetinaNet for indoor navigation [13] with obstacle detection.…”

“…A cloud based wearable assistive aid built using convolutional transformer with weak-attention suppression. A novel approach of navigation based on speech recognition was presented [12]. It provided traffic light detection, obstacle avoidance, payment, and navigation assistance.…”

Intelligent Head-Mounted Obstacle Avoidance Wearable for the Blind and Visually Impaired

“…The limitation of the system includes high energy consumption and a short battery life [25]. Xia et al integrated a laser ranging chip with an image processing unit to enable the recognition of traffic lights, obstacle avoidance, and payment functionalities [26]. This system utilises a YOLO-lite algorithm; however, it is still significantly power-consuming with an average current of 226.92 mA per second [26].…”

“…Xia et al integrated a laser ranging chip with an image processing unit to enable the recognition of traffic lights, obstacle avoidance, and payment functionalities [26]. This system utilises a YOLO-lite algorithm; however, it is still significantly power-consuming with an average current of 226.92 mA per second [26]. A common challenge in all vision-sensor-based works mentioned above is the significant computational cost associated with carrying out the algorithm or device, which typically requires the user to have a high-performance laptop with them.…”