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Calculating Heart Rate Variability from ECG Data from Youth with Cerebral Palsy During Active Video Game Sessions

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Detecting and Predicting Pilot Mental Workload Using Heart Rate Variability: A Systematic Review.

Peizheng Wang¹, Robert Houghton¹, Arnab Majumdar¹

¹Centre for Transport Studies, Department of Civil and Environmental Engineering, Imperial College London, London SW7 2AZ, UK.

Sensors (Basel, Switzerland)

|June 27, 2024

Summary

Measuring pilot mental workload (MWL) using heart rate variability (HRV) shows promise for aviation safety. However, current research presents inconsistent findings and diverse methods for HRV-based MWL detection systems.

Keywords:

ECG heart rate variability mental workload physiological signals wearable devices

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Area of Science:

Aviation Psychology
Biomedical Engineering
Human Factors Engineering

Background:

Pilot mental workload (MWL) is critical for aviation safety, but traditional assessment methods face challenges in automated cockpits.
Heart rate variability (HRV) is a promising physiological indicator for assessing MWL during flight.
Existing research on HRV and MWL exhibits considerable variability in study designs and measurement techniques.

Conclusions:

The use of HRV for MWL assessment in aviation is promising but requires standardization.
Machine learning offers advanced solutions for developing robust HRV-based MWL detection systems.
Further research is needed to reconcile inconsistent findings and optimize HRV application in aviation safety.