Matngon: an Ar-Integrated Machine Learning Application for Proactive Risk Detection and Management

The fusion of digital and physical worlds through augmented reality (AR) has transformed human interaction with information and the environment, all while prioritizing design for humanity to ensure accessibility, inclusivity, and well-being. This study investigated an AR-incorporated machine learning application named “matngon” aimed at detecting and mitigating everyday risks such as stairs, windows, scissors, knife, outlet and doors. Anchored on the Protection Motivation Theory and the Technology Acceptance Model, the study aimed to address the unavailability and lack of accessibility and usability of AR applications (apps) for risk detection, particularly countries like the Philippines. The system integrates object detection algorithms using Tensorflow.js with real-time camera feeds on mobile devices to provide proactive hazard identification and warnings. Additionally, it provides features like first aid modules and an emergency contacts list to make it an integrated risk management application. The performance and functionality testing of the app demonstrates efficient execution of various features and successful completion of risk detection which is also reflected in the users' positive perceptions regarding the application's usefulness, ease of use, and intention to adopt based on the user testing and interviews. However, the considerations of the following are recommended for future research: enhancing detection accuracy by implementing transfer learning for model refinement, expanding detection classes, and improving user interface and additional features to enhance user experience and accessibility. Thus, matngon demonstrates promising potential as a proactive risk detection and management tool, with scope for further refinement and enhancement to ensure usability and effectiveness in promoting safety and well-being.