Resonix - Environmental Sound Classification and Haptic Alert System for Hearing-Impaired Assistance
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Hearing-impaired individuals often face difficulty in recognizing important environmental sounds such as sirens, alarms, and warning signals, which can affect their safety and situational awareness in daily life. This paper presents Resonix, an AI-based wearable assistive system designed to identify critical environmental sounds and provide real-time haptic alerts through vibration patterns. Environmental sound datasets including ESC-50 and UrbanSound8K were utilized for model development. Five relevant sound classes — siren, dog bark, alarm, baby cry, and glass break — were selected for classification. Audio preprocessing techniques such as normalization and noise reduction were performed before training the models. Multiple machine learning models including KNN, SVM, Random Forest, and a Convolutional Neural Network (CNN) were evaluated for environmental sound classification. Experimental results showed that the proposed CNN model achieved the highest accuracy of 93.0% compared to other models. The trained model was integrated with an ESP32-based wearable setup consisting of three microphone modules, two vibration motors, and an emergency push button for real-time haptic alert generation. The proposed system aims to improve environmental awareness and assist hearing-impaired individuals through a simple and low-cost wearable solution.
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