# Measuring Cognitive Workload Using Multimodal Sensors

arXiv: https://arxiv.org/abs/2205.04235 · HTML: https://mlsanity.com/paper/2205.04235/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 0 of its 14 references that are in the map.
- First posted on arXiv 5 May 2022.

## Built on by (most cited first, 3 of 3)

- [Towards a Unified Modality-Agnostic Multimodal Framework for Cognitive Workload Assessment](https://mlsanity.com/paper/2609.20199/) (2026)
- [One-Block Transformer (1BT) for EEG-Based Cognitive Workload Assessment](https://mlsanity.com/paper/2605.00856/) (2026)
- [Assessing Region-Level EEG Contributions to Cognitive Workload Prediction](https://mlsanity.com/paper/2606.02598/) (2026)

## Abstract (arXiv)

This study aims to identify a set of indicators to estimate cognitive workload using a multimodal sensing approach and machine learning. A set of three cognitive tests were conducted to induce cognitive workload in twelve participants at two levels of task difficulty (Easy and Hard). Four sensors were used to measure the participants' physiological change, including, Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and blood oxygen saturation (SpO2). To understand the perceived cognitive workload, NASA-TLX was used after each test and analysed using Chi-Square test. Three well-know classifiers (LDA, SVM, and DT) were trained and tested independently using the physiological data. The statistical analysis showed that participants' perceived cognitive workload was significantly different (p<0.001) between the tests, which demonstrated the validity of the experimental conditions to induce different cognitive levels. Classification results showed that a fusion of ECG and EDA presented good discriminating power (acc=0.74) for cognitive workload detection. This study provides preliminary results in the identification of a possible set of indicators of cognitive workload. Future work needs to be carried out to validate the indicators using more realistic scenarios and with a larger population.