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Continuous monitoring of stress on smartphone using heart rate variability

Continuous monitoring of an individual's stress levels is essential to manage stress and mental state in an effective way. With increasing ubiquity of wearable heart rate monitors and their unobtrusiveness, HRV (Heart rate variability) derived from heart rate signals has emerged as one of the most relevant parameters for continuous monitoring of stress. In the present work, we have made an attempt to address the challenges about distinguishing between stressed and non-stressed state of a person based on just one minute of IBI (Inter Beat Interval) records with good accuracy. Such ultra-short term analysis of HRV is particularly advantageous towards capturing very short term fluctuations in mental stress levels and enhanced scope for frequent monitoring. We have analyzed various time domain, frequency domain and nonlinear HRV features to narrow down to a most influential set of features for accurate classification between stressed and non-stressed state. We have identified RMSSD (root mean square of successive differences) of IBI series to be the most direct indicator of stressed state. We also provide a continuous stress score which, when used in continuous monitoring scenario, provides the user with adequate details about his/her stress levels. This helps the user to understand stress patterns across a day in a better way and to take appropriate measures to manage stressful situations. We have developed and deployed a system, based on above concept, on smartphone as an android application for real-time stress monitoring.