  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.

Neurons and Cognition;Artificial Intelligence;Human-Computer Interaction