Real-Time Biometric Monitoring for Cognitive Workload Detection: A Narrative Review of Applications in High-Demand Professions

O'Hara, Reginald B.; Loftis, Shelby Chase; Rando, Cynthia · 2025 · Crossref

DOI: 10.1101/2025.08.28.25334668

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Summary

This narrative review addresses the critical need for real-time (RT) biometric monitoring systems to detect and manage cognitive workload in high-demand professions. The authors were motivated by the increasing prevalence of mental fatigue and error rates in sectors such as healthcare, construction, and public safety, where rapid technological advancements and multitasking demands often exceed human cognitive reserves. The paper aims to evaluate the theoretical foundations of mental workload, assess various biometric modalities, and explore the integration of artificial intelligence (AI) and machine learning (ML) to enhance adaptive task scheduling and worker safety. The study employed a narrative review methodology, sourcing literature from Google Scholar, PubMed Central, and university databases between 1981 and 2025. The authors applied strict inclusion criteria, focusing on open-access, peer-reviewed articles involving high-demand occupations, multimodal data, and biometric sensors. After screening thousands of records, 39 sources were selected for analysis, including primary research, systematic reviews, and technical reports. The review synthesizes findings on physiological measures (e.g., heart rate variability, EEG, electrodermal activity), behavioral indicators (e.g., eye tracking, reaction time), and subjective assessments (e.g., NASA-TLX, Bedford Scale). Key findings indicate that multimodal data integration offers a more comprehensive and accurate assessment of cognitive load than single-source metrics. Physiological signals like heart rate variability and EEG provide objective, continuous data on stress and mental effort, while subjective tools offer context-specific insights. The review highlights a case study involving human-robot interaction in a smart factory, where EEG and functional Near-Infrared Spectroscopy (fNIRS) detected peak cognitive overload during rapid task sequences, demonstrating the feasibility of RT monitoring. However, the authors note significant challenges, including sensor reliability, calibration consistency, individual variability in stress responses, and ethical concerns regarding data privacy and algorithmic bias. The significance of this work lies in its demonstration that integrating biometric data with AI-driven analytics can enable early warning systems for cognitive overload. Such systems could inform adaptive scheduling, optimize work-recovery cycles, and reduce error rates in time-critical environments. The authors conclude that while the technology is promising, further longitudinal empirical research is required to validate sensor accuracy and AI predictions in real-world operational contexts before large-scale deployment in sectors like air traffic control and industrial operations.

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Topics

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• workload measurement
• neuro workload indices
• mental demand

Information type

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• Empirical Findings: physiological data
• Theoretical Contribution: theory or model