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Estimate the Cognitive Load Using Electrocardiographic Measure: A Human-AI Collaborative Task

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Real-time cognitive workload assessment using non-intrusive methods: a systematic review.

Niosh Basnet¹, Maryam Zahabi¹

¹Wm Michael Barnes '64 Department of Industrial and Systems Engineering, Texas A&M University, College Station, TX, USA.

|December 31, 2025

Summary

Real-time cognitive workload (CWL) assessment uses physiological and behavioral monitoring, predominantly with wearable devices. Findings guide the selection of measurement tools and computational models for enhanced human performance and safety.

Keywords:

Cognitive workload machine learning non-intrusive physiological measurement real-time

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Area of Science:

Human-Computer Interaction
Cognitive Science
Biomedical Engineering

Background:

Real-time cognitive workload (CWL) assessment is crucial for improving human performance and safety in demanding operational environments.
Monitoring CWL aids in understanding operator states and optimizing system design.
Existing research highlights the need for comprehensive reviews of CWL assessment methodologies.

Purpose of the Study:

To synthesize findings from 50 peer-reviewed studies on physiological and behavioral CWL monitoring.
To examine current practices, methodological trends, and technological advancements in CWL assessment.
To provide guidelines for measurement and model selection in real-world CWL system deployment.

Main Methods:

Systematic review of 50 peer-reviewed studies on cognitive workload assessment.
Analysis of physiological measures including electrocardiography (ECG), photoplethysmography (PPG), electrodermal activity (EDA), electroencephalography (EEG), and skin temperature (SKT).
Inclusion of behavioral measures such as eye-tracking and analysis of computational approaches (machine learning, deep learning, hybrid models).

Main Results:

Wearable devices were predominantly used (approximately 74%) for CWL monitoring.
Physiological measures showed alignment with different task demands (cognitive, perceptual, motor, physical).
Traditional machine learning (32%) and statistical models (21%) were favored over deep learning (14%), with a notable trend towards hybrid approaches (22%).

Conclusions:

The review provides a comprehensive overview of current CWL assessment practices and technological trends.
Guidelines for selecting appropriate measurement techniques and computational models are offered based on task requirements.
Future research should focus on adaptive frameworks for seamless real-world CWL system implementation to enhance human performance and safety.