  The construction industry has demonstrated a high frequency and severity of
accidents. Construction accidents are the result of the interaction between
unsafe work conditions and workers unsafe behaviors. Given this relation,
perceived risk is determined by an individual response to a potential work
hazard during the work. As such, risk perception is critical to understand
workers unsafe behaviors. Established methods of assessing workers perceived
risk have mainly relied on surveys and interviews. However, these post-hoc
methods, which are limited to monitoring dynamic changes in risk perception and
conducting surveys at a construction site, may prove cumbersome to workers.
Additionally, these methods frequently suffer from self-reported bias. To
overcome the limitations of previous subjective measures, this study aims to
develop a framework for the objective and continuous prediction of construction
workers perceived risk using physiological signals [e.g., electrodermal
activity (EDA)] acquired from workers wristband-type biosensors. To achieve
this objective, physiological signals were collected from eight construction
workers while they performed regular tasks in the field. Various filtering
methods were applied to exclude noises recorded in the signal and to extract
various features of the signals as workers experienced different risk levels.
Then, a supervised machine-learning model was trained to explore the
applicability of the collected physiological signals for the prediction of risk
perception. The results showed that features based on EDA data collected from
wristbands are feasible and useful to the process of continuously monitoring
workers perceived risk during ongoing work. This study contributes to an
in-depth understanding of construction workers perceived risk by developing a
noninvasive means of continuously monitoring workers perceived risk.

Signal Processing;Human-Computer Interaction