Prescriptive analytics for impulsive behaviour prevention using real-time biometrics

• Published in: Progress in artificial intelligence Vol. 10; no. 2; pp. 99 - 112
• Main Authors: Raychaudhuri, Soumya Jyoti, Manjunath, Soumya, Srinivasan, Chithra Priya, Swathi, N., Sushma, S., Nitin Bhushan, K. N., Narendra Babu, C.
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.06.2021; Springer Nature B.V
• Subjects: Algorithms; Artificial Intelligence; Bayesian analysis; Biometrics; Computational Intelligence; Computer Imaging; Computer Science; Control; Data Mining and Knowledge Discovery; Deep learning; Lightweight; Machine learning; Market segments; Mechatronics; Natural Language Processing (NLP); Pattern Recognition and Graphics; Real time; Regular Paper; Robotics; Segments; Sensors; Tracking devices; Vision; Wearable technology; Biometrics; Variational autoencoders; Bayesian prediction; Artificial intelligence; Convolutional neural network
• ISSN: 2192-6352; 2192-6360
• DOI: 10.1007/s13748-020-00229-9

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