Risk perception modeling based on physiological and emotional responses / Ding Huizhe
Risk perception refers to how individuals perceive objective risks. Although it initially emerged in social sciences, it has become a crucial aspect of safety science due to its significance in understanding unsafe behaviors. It can help safety managers develop a comprehensive understanding of ri...
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| Format: | Thesis |
| Published: |
2024
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| Online Access: | http://studentsrepo.um.edu.my/15614/1/Ding_Huizhe.pdf http://studentsrepo.um.edu.my/15614/2/Ding_Huizhe.pdf http://studentsrepo.um.edu.my/15614/ |
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| Summary: | Risk perception refers to how individuals perceive objective risks. Although it initially
emerged in social sciences, it has become a crucial aspect of safety science due to its
significance in understanding unsafe behaviors. It can help safety managers develop a
comprehensive understanding of risk based on traditional engineering risk assessment
principles, facilitating the transition from the Safety I to Safety II paradigm. Therefore,
accurate risk perception has become vital. This research aims to develop models for
objectively assessing perceived risk. Previous studies have employed machine learning
techniques to classify high and low-risk situations based on physiological responses.
However, the performance of these algorithms in situations with closely comparable risk
magnitudes remains uncertain. This issue is crucial as it directly impacts their practicality
and generalization. To address this concern, four driving clips were selected as stimuli,
including relatively low (1.87) and high (3.97) risk levels, as well as two clips with slight
variations in their degree of riskiness (2.45 and 2.85, respectively). Fifty-five subjects
were recruited to synchronously measure their physiological signals, including
Electrodermal Activity (EDA), Heart Rate Variability (HRV), Pupil Diameter (PD), and
Skin Temperature (ST). A Pleasure-Arousal-Dominance (PAD) model was used to
induced and expressed mixed emotions. Subsequently, statistical analyses were
performed to identify indicators that showed significant differences. These results varied
significantly, including three emotional dimensions, two skin conductance indicators
(EDR and EDL), and several ECG indicators (such as HF, LF/HF and A++) reflecting
short-time changes. As the perceived risk level increased, subjects’ emotions experienced
more negative, arousal and a diminished sense of control. In terms of physiological
changes, there was an increase in sympathetic activity and a concurrent decline in the vagus nerve at a macro-level. However, the changes that resulted from consecutive
heartbeats were characterized by rapid and erratic variations at a micro-level.
Additionally, these observed significant differences were primarily attributed to
variations in risk levels, rather than personal differences. In terms of feature importance,
physiological and emotional indicators that showed significant differences or greater
fluctuations demonstrated greater sensitivity. Finally, three base models, Artificial Neural
Network (ANN), Random Forest (RF), Support Vector Classification (SVC), and two
integrated models were trained to classify perceived risk using higher sensitivity features.
The ANN demonstrated superior ability in distinguishing low and high-risk levels.
However, when risk degrees were closely matched, the integrated model with weight
adjustments based on base models outperformed ANN. To validate the research findings,
a second experiment was conducted in a construction scenario, still utilizing two clips
with closely matched risk degrees. It was demonstrated that the primary results derived
from statistical analysis and machine learning modelling were remarkably consistent,
thereby confirming the effectiveness and generalization of the proposed weight
adjustment algorithm, particularly in situations with closely matched risk levels.
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