Design and Implementation of Environmental Warning Systems for Drivers with Hearing Impairments

Abstract

Hearing impairment encompasses conditions where individuals experience difficulties in hearing, categorized into deafness and hard of hearing. The employment opportunities provided by online taxi companies in Indonesia, particularly for motor or car drivers, offer a sense of independence to those with hearing impairments. However, driving necessitates environmental awareness, especially of auditory signals. Hence, there's a need for a surrounding environment warning system to convert crucial sounds into visual alerts, focusing on sounds like sirens and railway crossing warnings. This study introduces a warning system for drivers with hearing impairments, utilizing Recurrent Neural Network and Long Short Term Memory (RNN-LSTM) methods, implemented on a Raspberry Pi. The system, comprising a Raspberry Pi 4, microphone, LCD TFT, and LEDs, processes captured siren sounds into text alerts on the LCD and blinking LED signals. Testing the system with different sound recognition durations—2, 3, and 4 seconds—yielded accuracies of 78%, 82%, and 91%, respectively. Results indicate that prediction accuracy is influenced by the duration of sound recognition. Future research could explore enhancements with higher RAM Raspberry Pi 4 and more sensitive microphones to reduce noise.