MoRSE: Deep Learning-based Arm Gesture Recognition for Search and Rescue Operations
Abstract
Efficient and quick remote communication in search and rescue operations can be life-saving for the first responders. However, while operating on the field means of communication based on text, image and audio are not suitable for several disaster scenarios. In this paper, we present a smartwatch-based application, which utilizes a Deep Learning (DL) model, to recognize a set of predefined arm gestures, maps them into Morse code via vibrations enabling remote communication amongst first responders. The model performance was evaluated by training it using 4,200 gestures performed by 7 subjects (cross-validation) wearing a smartwatch on their dominant arm. Our DL model relies on convolutional pooling and surpasses the performance of existing DL approaches and common machine learning classifiers, obtaining gesture recognition accuracy above 95%. We conclude by discussing the results and providing future directions.
Keywords
Cite
@article{arxiv.2210.08307,
title = {MoRSE: Deep Learning-based Arm Gesture Recognition for Search and Rescue Operations},
author = {Panagiotis Kasnesis and Christos Chatzigeorgiou and Dimitrios G. Kogias and Charalampos Z. Patrikakis and Harris V. Georgiou and Aspasia Tzeletopoulou},
journal= {arXiv preprint arXiv:2210.08307},
year = {2022}
}
Comments
Accepted for presentation in the IEEE 8th World Forum on Internet of Things