ReviewSensors (Basel, Switzerland)2026
Non-Invasive Physiological Metrics for Cognitive Load Assessment in Training and Operational Contexts: Signal Processing, Evidence, and Feasibility.
Review in Sensors (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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0 citing papers in PubMed.
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Authors and funding
2 authors.
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No grant is acknowledged in the PubMed record.
Abstract
Cognitive load is a key determinant of performance, safety, and learning in high-stakes environments. Self-report and performance-based methods remain valuable but often miss rapid within-task changes in mental demand, motivating non-invasive physiological sensing for continuous monitoring. The interpretability of these signals depends on their specificity, acquisition quality, signal processing requirements, and real-world feasibility. Integrating physiological mechanism, measurement performance, and operational feasibility, this article reviews non-invasive physiological metrics, including cardiovascular measures (HR/HRV), respiratory metrics, EEG, fNIRS, ocular metrics, and electrodermal activity, for cognitive load assessment across training, simulated, and operational contexts. This structured narrative review synthesized 37 sources published between January 2021 and February 2026, including 25 primary empirical studies and 12 systematic reviews or meta-analyses identified through Google Scholar, PubMed, Scopus, Web of Science, and IEEE Xplore. Among the primary studies, cardiovascular measures were most frequently used (HR/HRV, 16 of 25), followed by EEG (11), ocular metrics (9), electrodermal activity (8), fNIRS (3), and respiratory metrics (3). A consistent trade-off emerged between physiological specificity and ease of deployment. EEG frontal theta showed the most direct and meta-analytically supported link to cortical processing, but it is constrained outside controlled settings by motion artifacts and setup demands. Cardiovascular and electrodermal signals deploy easily through wearables but reflect broader autonomic or sympathetic activation rather than cognitive load specifically. No single metric reviewed here provides both high specificity and strong field readiness. A more defensible approach pairs signals deliberately, based on complementary mechanisms, signal-processing burden, and deployment context, rather than adding sensors indiscriminately. A tiered decision framework and an iterative, context-aware synthesis are proposed to guide metric selection and the interpretation of complementary measurements over time.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.