Development of a Real-Time Hand Gesture Recognition System for Aid of Hearing-Impaired Communication Using Flex Sensors and Machine Learning Algorithms

The word “Gestures” conjures images of persons who are deaf or hearing-impaired. The main reason is that their primary and only mode of communication is based on gestures. Only hearing-impaired people have an understanding of gestures in today’s world. As a result, communicating with regular people has become a challenging chore for them. Consequently, there is a significant communication gap between normal people and those who are hard of hearing. All of this is taken into account, and the purpose of the research is to find a successful solution for hearing-impaired persons to communicate with one another. Here, a glove equipped with flex sensors was used to create a novel real-time gesture detection system based on machine learning. The application of several algorithms, such as the Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Naive Bayes algorithms, might be recognized as a specialty to address this classification problem. The system has been able to achieve a more accurate and precise result by successfully displaying ten messages corresponding to the detected gesture on the Graphical User Interface (GUI). Additionally, this system has the ability to gradually expand the quantity of messages that are communicated by gestures based on the requirements of the users. Due to its wireless connectivity, ease of use, quick response time, and flexibility to meet the user’s demands, this system is probably to become a more popular and beneficial communication aid among individuals with hearing loss in the future.