Systematic review of neurophysiological assessment techniques and metrics for mental workload evaluation in real-world settings
DOI: 10.3389/fnrgo.2025.1584736
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Summary
This systematic review addresses the lack of a unified framework for evaluating mental workload (MWL) in real-world occupational settings. While digitalization has reduced physical demands, it has increased cognitive demands, leading to errors, accidents, and psychological health issues. Existing literature often conflates laboratory and field data, yet field studies show significantly higher heart rate variability (up to 50% vs. 10% in labs), making direct transfer of laboratory metrics unreliable. The study aims to identify objective, validated neurophysiological methods that allow for real-time MWL assessment without disrupting operator activity, providing a comprehensive mapping of application domains for occupational safety specialists. The authors conducted a systematic search following PRISMA guidelines across PubMed, ScienceDirect, and IEEE Xplore, covering publications from inception to March 30, 2023. Inclusion criteria required studies to evaluate MWL in occupational settings using at least one physiological measure in real or near-real work contexts with actual workers. Purely theoretical reviews, laboratory-only studies with naïve participants, and driving-specific studies (already extensively reviewed elsewhere) were excluded. Using the Rayyan software for screening, the team retained 35 studies for final analysis. Data were compiled into a synthesis table to analyze publication trends, application domains, and specific measurement techniques. Results indicate that approximately 70% of the included studies were published after 2016, reflecting the rise of portable sensor technology. The most represented sectors were industry (34%), aviation (17%), construction (14%), maritime (12%), and medical (11%). Electrocardiogram (ECG) and heart rate variability (HRV) were the most frequently employed techniques, often combined with eye-tracking, electroencephalography (EEG), and electrodermal activity (EDA). Specific findings include established heart rate thresholds for workload classification (e.g., 100–125 bpm indicating moderate workload) and the utility of HRV metrics like LF/HF ratios and RMSSD in distinguishing stress levels. EEG analyses highlighted increased frontal theta and decreased parietal alpha power as indicators of high cognitive load. The review emphasizes that physiological measures are frequently combined with subjective assessments to improve validity. The significance of this work lies in its provision of a practical guide for ergonomists and safety professionals to select appropriate neurophysiological tools for field applications. By distinguishing between laboratory and field data, the review highlights the necessity of context-specific calibration. Understanding the interrelations between MWL metrics and stress indicators is crucial for anticipating impacts on worker mental health and safety. The study concludes that while no single method is universally superior, a multi-modal approach combining objective physiological data with subjective reports offers the most robust framework for real-time MWL monitoring in diverse industrial environments.
Provenance
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| Stage | Outcome | Tool | Model | Prompt | Attempts | Completed |
|---|---|---|---|---|---|---|
| discover | success | Crossref | — | — | 1 | 2026-08-09 |
| archive | success | canonical_url | — | — | 1 | 2026-08-09 |
| extract | success | cached | — | — | 4 | 2026-08-23 |
| clean | success | clean | — | — | 1 | 2026-08-09 |
| chunk | success | chunk | — | — | 1 | 2026-08-09 |
| embed | success | embed | Qwen/Qwen3-Embedding-8B | — | 1 | 2026-08-09 |
| promote | success | — | — | — | 1 | 2026-08-09 |
| summarize | success | llm | qwen3.8-27b-gittensor | summ-v5 | 3 | 2026-08-23 |
| tag | success | vector_similarity | — | — | 11 | 2026-08-11 |
| verify | partial | — | — | — | 2 | 2026-08-09 |
Summary generated by qwen3.8-27b-gittensor on 2026-08-23; verification: pending re-verification.
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Information type
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- Empirical Findings: physiological data, self report data
- Methodological Resource: metric or index