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Related Concept Videos

Stress Concentrations01:13

Stress Concentrations

The concept of stress concentration is crucial for understanding how materials respond under bending stresses, particularly when there are irregularities or discontinuities in the material's geometry. Normally, stress in a symmetric member subjected to pure bending is assumed to be uniformly distributed across the entire cross-section. However, this assumption does not hold when there are variations in the cross-sectional geometry or the presence of notches and holes.
The stress concentration...
Stress Concentrations01:24

Stress Concentrations

Stress concentration is when stress intensifies near discontinuities such as holes or abrupt cross-sectional changes in a structural member. This localized stress can often surpass the average stress within the member. The stress distribution in flat bars, either with a circular hole or varying widths connected by fillets, can be determined experimentally using a photoelastic method. The results are based on ratios of geometric parameters like the ratio of the hole's radius to the smaller width...
Exercise Stress Test01:26

Exercise Stress Test

Introduction
Exercise stress testing, commonly known as a treadmill test, is a noninvasive procedure used to evaluate cardiovascular function and diagnose heart conditions.
Definition
An exercise stress test measures the heart's response to exertion using a treadmill or stationary bicycle. Chest electrodes record the heart's electrical activity through an ECG, and blood pressure is monitored regularly.
Purposes

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Evaluation of Commercial-Off-The-Shelf Wrist Wearables to Estimate Stress on Students
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Consumer-Grade Wearable Sensors for Classifying Pilot Workload and Stress During Real Flight Training: A

Rongbing Xu1,2, Shi Cao1,2, Michael Barnett-Cowan2,3

  • 1Department of Systems Design Engineering, University of Waterloo, Waterloo, ON N2L 3G1, Canada.

Sensors (Basel, Switzerland)
|June 26, 2026
PubMed
Summary

Consumer-grade wearable sensors show potential for monitoring pilot workload and stress during real flight training. While cross-pilot prediction remains modest, personalized classification may be feasible after calibration for near-term applications.

Keywords:
aviation human factorsleave-one-subject-outphysiological signalspilot stresspilot workloadreal flightwearable sensors

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Area of Science:

  • Physiological monitoring
  • Human factors in aviation
  • Wearable sensor technology

Background:

  • Prior studies on pilot workload and stress monitoring often use simulators, limiting real-world applicability.
  • Consumer-grade wearables offer continuous monitoring potential but require validation in actual flight conditions.
  • Existing research faces challenges with generalizability due to varied validation methods and labeling techniques.

Purpose of the Study:

  • To evaluate the efficacy of wearable sensors (Empatica Embrace Plus, Polar H10) in classifying pilot workload and stress during real flight training.
  • To assess electrodermal activity (EDA), ECG-derived features, and wrist skin temperature for workload and stress deviation detection.
  • To compare the generalizability of different machine learning classifiers using leave-one-subject-out cross-validation.

Main Methods:

  • Thirty-five pilots underwent real Cessna 172 flight training, with physiological data collected continuously.
  • Workload and stress were self-reported by pilots after each flight segment.
  • Fold-safe two-way residual binary labels were used to mitigate inter-pilot variability and task effects.
  • Five machine learning classifiers were evaluated using leave-one-subject-out (LOSO) cross-validation with FDR correction.

Main Results:

  • Linear SVC and XGBoost models achieved significant above-chance classification for stress (macro F1 = 0.607) and workload (macro F1 = 0.598) respectively, under LOSO validation.
  • These results remained stable under nested cross-validation, indicating robustness.
  • Subject-dependent validation showed higher performance (e.g., macro F1 = 0.853 for stress), but was unstable in stricter analyses.
  • Uncalibrated cross-pilot prediction performance was modest in real-world flight scenarios.

Conclusions:

  • Consumer-grade wearables can classify pilot workload and stress deviations during actual flight training, albeit with modest cross-pilot generalizability.
  • Personalized classification models show promise for future applications after individual calibration.
  • Post-flight debriefing remains a plausible near-term application for this technology in aviation training.