Deep learning based Human Activity Recognition in first responders wearing a sensorized garment

Abstract

Safety and well-being of first responders operating in hazardous environments are paramount considerations. These individuals routinely find themselves immersed in dangerous situations, leading to heightened levels of both physical and mental stress. In this context, a system for the automatic and real-time monitoring of first responders' (FRs) activities could play an important role in timely identifying potentially dangerous situations. The present paper addresses this issue and introduces a Deep Learning (DL) based Human Activity Recognition (HAR) approach for the automatic identification of tasks carried out by first responders. In our proposed framework, we leverage the use of a garment equipped with various integrated sensors to capture both physiological and inertial measurements during the course of first responders' duties. For this aim we harness the power of DL techniques, specifically recurrent neural networks (RNNs), aiming at achieving an accurate classification of a limited set of diverse tasks. To validate the efficacy of our proposed system, we conducted the evaluations on a comprehensive hold-out set compiled from real-world scenarios, involving FRs. The results of our evaluation showcase not only high accuracy (0.9813) but also robust reliability in classifying the activities undertaken by the operators. The implications of our deep learning-based activity recognition framework extend beyond mere classification, since gaining insights into the risk associated to a particular task performed could enable the development of more effective, timely and safer emergency response strategies.

Keywords: Deep Learning, HAR, first responders, sensorized garments, wearable devices

DOI: 10.54941/ahfe1004701