Real-time detection of workload changes using heart rate variability

This work presents a novel approach to detecting real-time changes in workload using heart rate variability (HRV). We propose that for a given workload state, the values of HRV vary in a sub-range of a Gaussian distribution. We describe methods to monitor a HRV signal in real-time for change points based upon sub-Gaussian fitting. We tested our method on subjects sitting at a computer performing a low workload surveillance task and a high workload video game task. The proposed algorithm showed superior performance compared to the classic CUSUM method for detecting task changes.

► We detect real-time changes in mental workload using heart rate variability (HRV). ► Our novel approach models HRV in a sub-range of a Gaussian distribution. ► We tested on 45 subjects switching from a shooting game to a surveillance task. ► On an ROC curve our method shows superior performance to the classic CUSUM.