GajGamini: Mitigating Man–Animal Conflict by Detecting Moving Elephants Using Ground Vibration-Based Seismic Sensor

Abstract

We introduce “GajGamini:” a novel method for detecting elephant movement by analyzing ground vibrations recorded using seismic sensors. This method is based on the principle that ground vibrations from elephants are distinct from those caused by humans and background noise. In this letter, we address two main challenges. First, there was a lack of studies with extensive data on vibrations from Indian elephants and humans. To address this, we recorded 3 h of elephant movements and 2 h of human movements using seismic sensors. Second, there was a need for a dedicated architecture for the real-time classification of seismic vibrations from elephants, humans, and background noise. To overcome this, we propose a convolutional neural network (CNN)–based model named “GajGamini” that achieves a prediction accuracy of ${\sim}98.03\%$ with only 3 s of computational runtime for every 10 s of recorded data. GajGamini represents a significant advancement in wildlife monitoring, particularly for elephant conservation. It offers a noninvasive way to track elephant movements, enhancing the effectiveness of wildlife management strategies.