Heart Rate Variability (HRV) analysis is based on variability between each heartbeat which is used
as a diagnosis method for assessing the cardiovascular modulation of autonomic nerve system. Up to
now, most HRV analysis has been done offline. However, in many relevant applications, HRV should be
analyzed online such as the analysis of stress level and the detection of the drowsiness while driving. This
paper proposes an online analysis method which can be used in platforms for human robot cooperation.
This online analysis method based on a sliding Hurst window can be applied to estimate the heart status.
By the sliding Hurst series, the two indices, cumulative mean of Hurst series (CMHurst) and cumulative
standard deviation of Hurst series (CStdHurst) are introduced as indicators to distinguish heart health
status. Using this method, the hardware requirement is significantly low, and the execution time is short.
Some databases from the PhysioBank are used for test these indices. The results show this method can
distinguish between the groups who have normal rhythm and abnormal rhythm.