Authors:
Shafait Azam
;
Mashnunul Huq
and
Andreas Pech
Affiliation:
Department of Computer Science and Engineering, Frankfurt University of Applied Sciences, Frankfurt am Main, Germany
Keyword(s):
Ultrasonic Sensor, Vector Embeddings, Residual Neural Network, Signal Processing, Human Computer Interaction, Pattern Recognition, Transfer Learning.
Abstract:
Ultrasonic sensors emitting ultrasound waves can be effectively used in Human Computer Interaction (HCI) to assist visually disabled humans. With the embedding of the sensor echoes into assistive tools, real-time spatial awareness for mobility is enhanced. Moreover, material identification aids object recognition by detecting different materials through their echo signatures. In this article, we study the use of ultrasonic sensors in HCI systems focusing on their ability to detect materials by analysing the ultrasonic wave characteristics. These services aim to improve the autonomy and security of people with visual impairments, offering a complete assistive solution for daily navigation and interaction processes. We have planned to create a vector database for storing these embeddings generated from reflected waves of various materials and objects. In this work, we propose a precise vector embeddings generation framework for ultrasonic systems using ResNet50 convolutional neural net
work. In the future, Generative AI will use these embeddings to serve a range of applications for greater autonomy and safety, providing an assistive travel and interaction solution for the visually impaired.
(More)