Prescriptive analytics for impulsive behaviour prevention using real-time biometrics

The present biometric market segment has been captured by compact, lightweight sensors which are capable of reading the fluctuations a user in real-time. This further facilitated rise new ecosystem wearable devices helpful tracking real-time physiological data for Healthcare-related analysis. However, smart-wearable limited to capturing and displaying biometrics without any prescriptive analytics. paper addresses this gap analyse human emotion space based on an individual’s state mind over past 60 min employs Deep Learning Bayesian prediction techniques predict possibility impulsive outburst within upcoming few minutes. A smart processing device mounted with captures calibrate same mental scale zero hundred. results reveal that deep learning algorithm along probability module can future mood lower error than other contemporary models. predicted matched actual changes experimental subject $$ \pm 10 time index 93% cases 5 82% cases.