Measuring Cognitive Workload Using Multimodal Sensors

TL;DR

This paper investigates using multimodal sensors and machine learning to identify indicators of cognitive workload, demonstrating that ECG and EDA data can effectively discriminate workload levels with 74% accuracy.

Contribution

It introduces a multimodal sensing approach combining ECG and EDA for cognitive workload detection, validated with machine learning classifiers and statistical analysis.

Findings

Participants' perceived workload differed significantly between tests.

ECG and EDA fusion achieved 74% accuracy in workload classification.

Preliminary indicators of cognitive workload were identified for future validation.

Abstract

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.