Citation

Sakib MN, Chaspari T, Behzadan AH. Smart Sustain. Built Environ. 2021; 11(4): 1017-1041.

Copyright

(Copyright © 2021, Emerald Group Publishing)

DOI

10.1108/SASBE-12-2020-0181

PMID

unavailable

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.

Language: en

Keywords

Accident prediction; Construction safety; Deep learning; Feedforward neural network; Physiological signal; Unmanned aerial vehicle