Hand Gesture Detection Technology using Raspberry Pi Converting Hand Signals into Tagalog Words

Abstract

This study is a Raspberry Pi-based system for translating hand gestures into Tagalog words for government facilities such as SSS, Pagibig, Phil Health, and City halls, aiming to provide guidance and support to the Filipino deaf community. The Filipino Sign Language (FSL) was declared the national sign language of the Filipino deaf by Rodrigo Duterte in 2018, mandating its use in educational institutions, broadcast media, and workplaces.

The researchers used an experimental prototyping method to conduct this study. Experimental prototyping is the process of developing a concept and prototype that can be accepted or rejected. For the survey methodology's demographic profile and descriptive method of interpreting the 5-point Likert Scale indications, the Purposive Sampling Technique has been used. The researchers will conduct a survey questionnaire to the product-targeted users. The Descriptive Type Survey Questionnaire Method analyzes data gathered to determine whether the intended users consider the prototype acceptable. Two sets of variables (15) Filipino Deaf people in Fairview, Quezon City, and (15) Normal people/Workplace employees in Novaliches, Quezon City, were chosen as respondents to these project proposals. Using Tinkercad, the researcher actually designed the prototypes in 2D and 3D. The prototype cover is made of ceramic glossy PVC wallpaper and plywood that measures 32cm in width and 23cm in height.

In the results of the testing using experimental prototyping, we discovered that the Raspberry Pi, as the microcomputer of our prototype hand gesture system, can translate hand gestures into Tagalog words, showing effective communication tools for Filipino deaf and normal people/workplace employees individuals. The Raspberry Pi-based Hand Gesture Detection Technology is a reliable innovation that helps Filipino deaf individuals communicate effectively.

The Hand Gesture Detection Technology Converting Hand Signals Into Tagalog Words is based on Filipino Sign Language (FSL). To develop a prototype using Raspberry Pi, bridging communication gaps and enhancing accessibility. The technology also aims to contribute to creating inclusive environments for individuals with disabilities, promoting social inclusion and equality. The system uses Python, MediaPipe, and Tensorflow frameworks for detection and gesture processes. The project demonstrates how CNN can be used to tackle computer vision problems accurately, potentially expanding other Filipino Sign Languages (FSL)