Automated Gesture-Recognition Solutions using Optimal Deep Belief Network for Visually Challenged People

Abstract

Gestures are a vital part of our communication. It is a procedure of nonverbal conversation of data which stimulates great concerns regarding the offer of human–computer interaction methods, while permitting users to express themselves intuitively and naturally in various contexts. In most contexts, hand gestures play a vital role in the domain of assistive technologies for visually impaired people (VIP), but an optimum user interaction design is of great significance. The existing studies on the assisting of VIP mostly concentrate on resolving a single task (like reading text or identifying obstacles), thus making the user switch applications for performing other actions. Therefore, this research presents an interactive gesture technique using sand piper optimization with the deep belief network (IGSPO-DBN) technique. The purpose of the IGSPO-DBN technique enables people to handle the devices and exploit different assistance models by the use of different gestures. The IGSPO-DBN technique detects the gestures and classifies them into several kinds using the DBN model. To boost the overall gesture-recognition rate, the IGSPO-DBN technique exploits the SPO algorithm as a hyperparameter optimizer. The simulation outcome of the IGSPO-DBN approach was tested on gesture-recognition dataset and the outcomes showed the improvement of the IGSPO-DBN algorithm over other systems.