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<p>This research developed and simulated a multi-indicator Fatigue Detection and Alert System (FDAS) designed to enhance fatigue detection accuracy in mining environments. The system integrates a set of inputs: eye blink rate, eye closure duration, yawning frequency, distraction levels, Pre-shift Check-in Scores and Electroencephalography (EEG) Levels. Using MATLAB's Fuzzy Logic Rule Viewer (FLRV) and Simulink, simulations demonstrated the FDAS's ability to process these diverse indicators accurately. It effectively classified operator fatigue into low, moderate, and high fatigue levels, consistently providing appropriate outputs across various mixed simulated signal scenarios. The system achieved a calculated accuracy of 85.19% across 27 simulated entries, with a standard deviation of approximately 1.84. These results suggest that the model performs consistently under the defined simulation conditions. The results suggest the model performs consistently under the defined simulation conditions. Using a mix of physiological, behavioral, and subjective indicators, the fuzzy logic approach can detect fatigue while reducing false warnings, as each indicator cross-validates the others. This approach shows promise for early detection of fatigue and may reduce false warnings, warranting further validation in operational environments.</p><p>Keywords: Fatigue Monitoring, Fuzzy Logic, Real-Time Fatigue Detection, Physiological Monitoring, Behavioral Analysis, Performance-Based Detection, Subjective Measurement, Self-Reported Indicators.</p>

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A Multi-Indicator Approach for Enhancing Real-time Worker Fatigue Monitoring in Mining Environments

  • 期刊论文

  • 发表日期：

    2026年7月22日

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  • Kojo Boakye,
  • Bright Oppong Afum,
  • Kwaku Boakye

摘要

This research developed and simulated a multi-indicator Fatigue Detection and Alert System (FDAS) designed to enhance fatigue detection accuracy in mining environments. The system integrates a set of inputs: eye blink rate, eye closure duration, yawning frequency, distraction levels, Pre-shift Check-in Scores and Electroencephalography (EEG) Levels. Using MATLAB's Fuzzy Logic Rule Viewer (FLRV) and Simulink, simulations demonstrated the FDAS's ability to process these diverse indicators accurately. It effectively classified operator fatigue into low, moderate, and high fatigue levels, consistently providing appropriate outputs across various mixed simulated signal scenarios. The system achieved a calculated accuracy of 85.19% across 27 simulated entries, with a standard deviation of approximately 1.84. These results suggest that the model performs consistently under the defined simulation conditions. The results suggest the model performs consistently under the defined simulation conditions. Using a mix of physiological, behavioral, and subjective indicators, the fuzzy logic approach can detect fatigue while reducing false warnings, as each indicator cross-validates the others. This approach shows promise for early detection of fatigue and may reduce false warnings, warranting further validation in operational environments.

Keywords: Fatigue Monitoring, Fuzzy Logic, Real-Time Fatigue Detection, Physiological Monitoring, Behavioral Analysis, Performance-Based Detection, Subjective Measurement, Self-Reported Indicators.