Design of rubble analyzer probe using ML for earthquake

Earthquakes are seismic events that result in the massive destruction of infrastructure, buildings, and human life. Life within each infrastructure is severely affected, and often people are trapped in wrecked building debris. An earthquake rubble analyzer is a probe designed to analyze the environment inside rubble and listen to ambient sounds. The designed probe senses human presence by detecting familiar sounds like “hello-help,” “breathe,” “muffled words,” “coughs,” and “noise” inside the rubbles using machine learning. The probe is designed to use a TinyML approach and a convolutional neural network (CNN) to classify these sounds with an accuracy of 97.45%. The probe provides real-time sensor data for different parameters like temperature, humidity, air quality, and pressure. The proposed probe can assess the survival prospects of people trapped within the rubble based on factors such as oxygen availability and body fluids retention.