My Watch Says I'm Busy: Inferring Cognitive Load with Low-Cost Wearables

To prevent undesirable effects of attention grabbing at times when a user is occupied with a difficult task, ubiquitous computing devices should be aware of the user's cognitive load. However, inferring cognitive load is extremely challenging, especially when performed without obtrusive, expensive, and purpose-built equipment. In this study we examine the potential for inferring one's cognitive load using merely cheap wearable sensing devices. We subject 25 volunteers to varying cognitive load using six different Primary tasks. In parallel, we collect physiological data with a cheap device, extract features, and then construct machine learning models for cognitive load prediction. As metrics for the load we use one subjective measure, the NASA Task Load Index (NASA-TLX), and two objective measures: task difficulty and reaction time. The leave-one-subject-out evaluation shows a significant influence of the task type and the chosen cognitive load metric on the prediction accuracy.