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

Journal of Biomedical Research & Environmental Sciences article abstract with citation details, DOI, publication dates, subject areas, full text links, and references.

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Open Access
Article Type Review Article
Subject General Science
OCLC JBRES Record
Reginald B OHara*, Shelby C Loftis and Cynthia Rando
Issue: Volume6-Issue11
Pages: 1764-1774
Received: 2025-11-19
Accepted: 2025-11-26
Published: 2025-11-28

Abstract

Background: Wearable technology and multidimensional data analysis present significant opportunities for the continuous, real-time monitoring of cognitive overload, potentially identifying early warning signals before performance degrades. However, key challenges exist, including ensuring data security, sensor accuracy, consistent calibration, accurate data processing, network limitations, and individual variability in responses.

Objectives: (1) To explore the theoretical foundations of mental workload, (2) to investigate methods for integrating data from multiple sources, (3) to evaluate the role of Machine Learning (ML) and Artificial Intelligence (AI) in predicting early indicators of cognitive overload, (4) to examine ethical, privacy, and security concerns related to AI and ML applications, and (5) to propose directions for future empirical research on the validity and reliability of real-time biometric monitoring.

Methods: Following the framework for Narrative Reviews (NRs) and the SANRA (Scale for the Assessment of NR articles) quality criteria, a comprehensive literature search was conducted across Google Scholar, PubMed Central, and electronic university databases from 1981 to 2025. Articles were selected based on relevance to the objectives and the primary aim using predefined search terms.

Results: After 27 articles were excluded for not meeting established inclusion criteria, a total of 66 articles were assessed for eligibility. After the final analysis, 39 full-text articles were included.

Conclusion: Integrating physiological and behavioral data with subjective assessments analyzed through AI and ML, may enable early detection of cognitive overload in high-stress environments. Such approaches could improve physical and cognitive performance, provide timely alerts for work-recovery cycles, and reduce task error rates. Presently, there is a lack of longitudinal studies addressing data standardization, sensor validation, and cybersecurity. Future empirical research is necessary to evaluate these technologies before their widespread use in critical sectors such as healthcare, public safety, air traffic control, and industry.

FullText HTML FullText PDF DOI: 10.37871/jbres2228 Crossmark

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Copyright

© 2025 Hara RBO, et al. Distributed under Creative Commons CC-BY 4.0 Creative CommonsAttribution

How to cite this article

O’Hara RB, Loftis SC, Rando C. Real-Time Biometric Monitoring for Cognitive Workload Detection in High Demand Professions: A Narrative Review. J Biomed Res Environ Sci. 2025 Nov 28; 6(11): 1764-1774. doi: 10.37871/jbres2228, Article ID: JBRES2228, Available at: https://www.jelsciences.com/articles/jbres2228.pdf

Subject area(s)

Biometrics

References

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