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1. Base paper Title: Real-Time Personalized Physiologically Based Stress Detection for
Hazardous Operations
Modified Title: Physiologically Based Real-Time Personalized Stress Detection for
Dangerous Operations
Abstract
When training for hazardous operations, real-time stress detection is an asset for
optimizing task performance and reducing stress. Stress detection systems train a machine-
learning model with physiological signals to classify stress levels of unseen data.
Unfortunately, individual differences and the time-series nature of physiological signals limit
the effectiveness of generalized models and hinder both post-hoc stress detection and real-time
monitoring. This study evaluated a personalized stress detection system that selects a
personalized subset of features for model training. The system was evaluated post-hoc for real-
time deployment. Further, traditional classifiers were assessed for error caused by indirect
approximations against a benchmark, optimal probability classifier (Approximate Bayes;
ABayes). Healthy participants completed a task with three levels of stressors (low, medium,
high), either a complex task in virtual reality (responding to spaceflight emergency fires, n =27)
or a simple laboratory-based task (N-back, n =14). Heart rate, blood pressure, electrodermal
activity, and respiration were assessed. Personalized features and window sizes were
compared. Classification performance was compared for ABayes, support vector machine,
decision tree, and random forest. The results demonstrate that a personalized model with time
series intervals can classify three stress levels with higher accuracy than a generalized model.
However, cross-validation and holdout performance varied for traditional classifiers vs.
ABayes, suggesting error from indirect approximations. The selected features changed with
window size and tasks, but found blood pressure was most prominent. The capability to account
for individual difference is an advantage of personalized models and will likely have a growing
presence in future detection systems.
Existing System
Despite extensive training in responding to an emergency, a person’s response to an
actual emergency can be negatively affected by the stressfulness of the situation. Stress can
result in a cascade of physiological changes that may alter behavioral patterns, situational

2. awareness, decision making, and cognitive resources [1]. An inability to cope with the stress
of a high-stress condition can decrease task performance and thereby risk mission failure,
injury, or death [2]. Consequently, developing resiliency to this situational stress through
improved training may lead to better outcomes. To that end, using real-time monitoring of a
person’s stress responses to customize the stressfulness of training scenarios may, in turn, lead
to more appropriate handling of actual hazardous operation [3], [4]. Stress detection using
machine learning has been challenging for several reasons. First, there are individual
differences in the appraisal of, and physiological responses to, stressful situations. Numerous
stress detection approaches have attempted to reduce technical complexity by generalizing their
models to a broad population, or the ‘‘average’’ response [3]. However, the stress response to
a unique situation is largely subjective, and personalized stress detection models may be more
robust to individual differences [5], [6]. The second challenge is that the time series nature of
physiological signals can be problematic. The physiological stress response has temporal and
feature correlations. These correlations may violate the machine learning assumption that the
data are independently and identically distributed, thereby leading to biased results [7].
Drawback in Existing System
 Individual Variability: People respond differently to stress, and physiological
indicators can vary significantly from person to person. Creating a one-size-fits-all
model may not be effective, and developing personalized models requires extensive
individual data, which may not always be feasible.
 Baseline Variability: Establishing an accurate baseline for an individual's
physiological parameters is challenging. Stress detection systems rely on deviations
from a baseline, and inaccuracies in determining the baseline can lead to false
positives or negatives.
 Ethical and Privacy Concerns: Continuous monitoring of physiological data raises
ethical concerns related to privacy. Individuals may feel uncomfortable with constant
monitoring of their physiological signals, and issues related to consent and data
ownership must be addressed.
 Cultural and Individual Differences: Cultural and individual differences in
expressing or experiencing stress may not be fully accounted for in stress detection
models, limiting the generalizability of the technology across diverse populations.

3. Proposed System
 Data Acquisition Module:
Collects raw physiological data from the sensors in real-time.
Ensures synchronization and time-stamping of data from multiple sensors.
 Personalized Stress Model:
Utilizes machine learning algorithms to build personalized stress models based on
historical data.
Takes into account individual variability and adapts to changes over time.
Considers contextual factors (environmental, task-related) to enhance accuracy.
 Integration with Hazardous Operations:
Interfaces with existing operational systems to enhance safety protocols.
Provides insights for supervisors and safety officers to monitor the stress levels of
individuals or teams.
Facilitates post-incident analysis and stress-related risk assessment.
 Continuous Improvement and Calibration:
Includes mechanisms for continuous system calibration based on user feedback and
system performance.
Allows for periodic updates and improvements to stress detection algorithms.
Algorithm
 Feature Extraction Algorithms:
Heart Rate Variability (HRV) Analysis: Extracts features from the interbeat
intervals of ECG signals to assess autonomic nervous system activity.
Respiration Rate Analysis: Determines respiratory patterns and extracts relevant
features from respiration signals.
 Integration with Stress Management Strategies:
Recommendation Systems: Suggests personalized stress management techniques
based on the detected stress levels.
Biofeedback Integration: Incorporates biofeedback mechanisms to provide real-
time feedback to individuals for stress regulation.

4.  Real-Time Monitoring and Thresholding:
Threshold-based Detection: Compares real-time physiological parameters with
personalized baseline values and triggers alerts when stress levels exceed predefined
thresholds.
Dynamic Threshold Adjustment: Adapts threshold values based on user-specific
characteristics and contextual factors.
Advantages
 Early Warning System:
Provides early detection of stress, allowing for timely intervention and risk
mitigation before stress levels escalate to a point where it could compromise safety
and performance.
 Real-Time Monitoring:
Monitors physiological signals continuously in real-time, providing a dynamic
assessment of stress levels during ongoing operations rather than relying on periodic
assessments. This ensures a more comprehensive understanding of the stress
dynamics.
 Preventive Stress Management:
Facilitates the implementation of preventive stress management strategies by
identifying stress triggers and patterns. This empowers individuals to adopt coping
mechanisms and resilience strategies proactively.
 Occupational Health Monitoring:
Contributes to long-term occupational health monitoring by tracking individuals'
stress levels over extended periods. This can aid in identifying chronic stress patterns
and implementing preventive measures.
Software Specification
 Processor : I3 core processor
 Ram : 4 GB
 Hard disk : 500 GB
Software Specification
 Operating System : Windows 10 /11
 Frond End : Python
 Back End : Mysql Server
 IDE Tools : Pycharm