Abstract
Search and Rescue (SaR) dogs are important assets in the hands of first responders, as they have the ability to locate the victim even in cases where the vision and or the sound is limited, due to their inherent talents in olfactory and auditory senses. In this work, we propose a deep-learning-assisted implementation incorporating a wearable device, a base station, a mobile application, and a cloud-based infrastructure that can first monitor in real-time the activity, the audio signals, and the location of a SaR dog, and second, recognize and alert the rescuing team whenever the SaR dog spots a victim. For this purpose, we employed deep Convolutional Neural Networks (CNN) both for the activity recognition and the sound classification, which are trained using data from inertial sensors, such as 3-axial accelerometer and gyroscope and from the wearable’s microphone, respectively. The developed deep learning models were deployed on the wearable device, while the overall proposed implementation was validated in two discrete search and rescue scenarios, managing to successfully spot the victim (i.e., obtained F1-score more than 99%) and inform the rescue team in real-time for both scenarios.
Highlights
We focus on the Dog Activity Recognition (DAR) for search and rescue (SaR) missions
We set the minimum number of epochs to 500; the training procedure terminated automatically whether the best training accuracy improved or not after a threshold of 100 epochs
We set the minimum number of epochs to 1000; the training procedure terminated automatically whether the best training accuracy had improved or not, after a threshold of 100 epochs
Summary
Animal Activity Recognition (AAR) and monitoring is an emerging research area enhanced mainly by the recent advances in computing, Deep Learning (DL) algorithms, and motion sensors. AAR attracted significant attention as it can provide significant insights about the behavior, health condition, and location of the observing animal [1]. If a proper network implementation is considered (e.g., with the proper devices, software, and communication protocol) the monitoring of the animal can be performed in real-time to allow exploitation of AAR for various purposes, e.g., study of the interaction between different animals, search and rescue missions [2], protection of animals from poaching and theft, etc. The use of inertial sensors is mandated, such as accelerometers, gyroscopes, and magnetometers as well as a Machine Learning (ML)
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