SynthesisSensors (Basel, Switzerland)2024
Detecting and Predicting Pilot Mental Workload Using Heart Rate Variability: A Systematic Review.
Synthesis in Sensors (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
17 citing papers in PubMed.
- Pilot Distractions and Interruptions in Airlines: Ranking of Sources by Analytic Hierarchy Process.Human factors · 2026Article
- ECG-Only Cognitive Workload State Classification in Laparoscopic Training Using Raw and Recurrence-Plot Representations.Sensors (Basel, Switzerland) · 2026Article
- Consumer-Grade Wearable Sensors for Classifying Pilot Workload and Stress During Real Flight Training: A Leave-One-Subject-Out Validation Study.Sensors (Basel, Switzerland) · 2026Article
- Measuring Pilot Mental Workload During Flights Without PAPI Lights Using a Wearable Device.Applied psychophysiology and biofeedback · 2026Article
- Multimodal Evaluation of Mental Workload and Engagement in Upper-Limb Robot-Assisted Motor Tasks.Sensors (Basel, Switzerland) · 2026Article
- Perceived workload and heart rate variability in elite military pilots recovering from operational maintenance and air demonstration flights.Frontiers in physiology · 2026Article
- Dynamic patterns of communication and workload during cardiac surgery: An explorative study.PloS one · 2026Article
- Comparative analysis of machine-learning methods for prediction of pilot performance during startle events from neuropsychophysiological features of stress resilience and cognitive task scores.Frontiers in physiology · 2026Article
- Multimodal workload assessment during simulator-based instrument flight training.Frontiers in psychology · 2026Article
- Examining physiological parameters of mental workload in the cockpit: a multimethod approach.Frontiers in physiology · 2026Article
- Article
- Machine learning evaluation model of pilot workload in a low-visibility environment.Scientific reports · 2025Article
- A narrative review on in-flight use of consumer sleep technologies for aviation research.Sleep advances : a journal of the Sleep Research Society · 2025Review
- Pilot mental workload analysis in the A320 traffic pattern based on HRV features.Frontiers in neuroergonomics · 2025Article
- Research on the evaluation model of pilots' in-flight stress state based on flight critical events.Frontiers in physiology · 2025Article
- Real-time monitoring of military health and readiness: a perspective on future research.Frontiers in digital health · 2025Article
- Variations in mood states among Brazilian air force pilots: a crossover analysis between operational and aerobatic flight missions.Frontiers in physiology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Measuring pilot mental workload (MWL) is crucial for enhancing aviation safety. However, MWL is a multi-dimensional construct that could be affected by multiple factors. Particularly, in the context of a more automated cockpit setting, the traditional methods of assessing pilot MWL may face challenges. Heart rate variability (HRV) has emerged as a potential tool for detecting pilot MWL during real-flight operations. This review aims to investigate the relationship between HRV and pilot MWL and to assess the performance of machine-learning-based MWL detection systems using HRV parameters. A total of 29 relevant papers were extracted from three databases for review based on rigorous eligibility criteria. We observed significant variability across the reviewed studies, including study designs and measurement methods, as well as machine-learning techniques. Inconsistent results were observed regarding the differences in HRV measures between pilots under varying levels of MWL. Furthermore, for studies that developed HRV-based MWL detection systems, we examined the diverse model settings and discovered that several advanced techniques could be used to address specific challenges. This review serves as a practical guide for researchers and practitioners who are interested in employing HRV indicators for evaluating MWL and wish to incorporate cutting-edge techniques into their MWL measurement approaches.
Indexed as
Identifiers
What OpenQuestion holds
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.