Sensing and assessing cognitive workload across multiple tasks

Abstract

Workload assessment models are an important tool to develop an understanding of an individual’s limitations. Finding times of excess workload can help prevent an individual from continuing work that may result in human performance issues, such as an increase in errors or reaction time. Currently workload assessments are created on a task by task basis, varying drastically depending on sensors and task goals. Developing independent models for specific tasks is time consuming and not practical when being applied to real-world situations. In this experiment we collected physiological signals including electroencephalogram (EEG), Heart Rate and Heart Rate Variability (HR/HRV) and Eye-Tracking. Subjects were asked to perform two independent tasks performed at two distinct levels of difficulty, an easy level and a difficult level. We then developed and compared performance of multiple models using deep and shallow learning techniques to determine the best methods to increase generalization of the models across tasks.