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A feedforward neural network for drone accident prediction from physiological signals

Md Nazmus Sakib (Multidisciplinary Engineering, Texas A&M University, College Station, Texas, USA)
Theodora Chaspari (Computer Science and Engineering, Texas A&M University, College Station, Texas, USA)
Amir H. Behzadan (Construction Science, Texas A&M University, College Station, Texas, USA)

Smart and Sustainable Built Environment

ISSN: 2046-6099

Article publication date: 9 June 2021

Issue publication date: 1 December 2022

317

Abstract

Purpose

As drones are rapidly transforming tasks such as mapping and surveying, safety inspection and progress monitoring, human operators continue to play a critical role in ensuring safe drone missions in compliance with safety regulations and standard operating procedures. Research shows that operator's stress and fatigue are leading causes of drone accidents. Building upon the authors’ past work, this study presents a systematic approach to predicting impending drone accidents using data that capture the drone operator's physiological state preceding the accident.

Design/methodology/approach

The authors collect physiological data from 25 participants in real-world and virtual reality flight experiments to design a feedforward neural network (FNN) with back propagation. Four time series signals, namely electrodermal activity (EDA), skin temperature (ST), electrocardiogram (ECG) and heart rate (HR), are selected, filtered for noise and used to extract 92 time- and frequency-domain features. The FNN is trained with data from a window of length t = 3…8 s to predict accidents in the next p = 3…8 s.

Findings

Analysis of model performance in all 36 combinations of analysis window (t) and prediction horizon (p) combinations reveals that the FNN trained with 8 s of physiological signal (i.e. t = 8) to predict drone accidents in the next 6 s (i.e. p = 6) achieved the highest F1-score of 0.81 and AP of 0.71 after feature selection and data balancing.

Originality/value

The safety and integrity of collaborative human–machine systems (e.g. remotely operated drones) rely on not only the attributes of the human operator or the machinery but also how one perceives the other and adopts to the evolving nature of the operational environment. This study is a first systematic attempt at objective prediction of potential drone accident events from operator's physiological data in (near-) real time. Findings will lay the foundation for creating automated intervention systems for drone operations, ultimately leading to safer jobsites.

Keywords

Acknowledgements

Some of the experiments described in this paper are conducted using an ANAFI drone, which was generously provided by Parrot, Inc. The authors would like to thank Mr. Jerome Bouvard, Director of Strategic Partnerships at Parrot Inc. Any opinions, findings, conclusions and recommendations expressed in this paper are those of the authors and do not represent the views of Parrot Inc.

Citation

Sakib, M.N., Chaspari, T. and Behzadan, A.H. (2022), "A feedforward neural network for drone accident prediction from physiological signals", Smart and Sustainable Built Environment, Vol. 11 No. 4, pp. 1017-1041. https://doi.org/10.1108/SASBE-12-2020-0181

Publisher

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Emerald Publishing Limited

Copyright © 2021, Emerald Publishing Limited

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