Hand Gesture Recognition using AI Algorithm for Hearing Impaired

HRI can be challenging to facilitate communication between humans and robots. This is because robots cannot understand human language without a mediator, and this causes problems for HRI when it comes to hearing impaired patients, and elderly persons. In today's world HGR plays an important role and the HGR system is widely used in many applications throughout the world. HGR can help establish transmission between humans and machines by helping these groups of people. ML is part of AI that focuses on the evolution of a system that rely on data. The main issue of HGR is that the machine doesn't recognize the human language directly and human machine interaction is in need of media for conveying which can be recognized by machine and as well as humans, to help the hearing impaired persons and elderly persons, so HGR as transmitting media is required to give instructions to computer. This project is based on HGR as an input method using two clustering methods, Fuzzy C Means clustering and Decision K Means clustering. The Euclidean distance will be used to compare the different data elements in order to get an output at the end of the project. The output had higher accuracies compared to other algorithms.