Impulsive Behaviour Detection System Using Machine Learning and IoT

Abstract

Wearable devices equipped with a multitude of compact, lightweight, biometric sensors are helpful in tracking the real-time physiological data for healthcare-related analysis. However, a survey of devices under the smart-wearable market segment revealed that the contemporary focus is limited to capturing and displaying some of the biometrics like pulse rate, movement of the user, calorie counter, etc. on a smart screen. Employing machine learning techniques can be particularly helpful in analyzing the trends of user-specific biometric data for pre-emptive actions. This paper presents a meaningful analysis in real-time by using machine learning approaches to interpret this physiological data and alert the user about his behavioral pattern through IoT. This research focuses on classifying the mood of the user as agitated or non-agitated, by analyzing the biometrics to help the user decipher meaningful conclusions and take suitable pre-emptive measures to refrain from any unintentional impulsive outburst. Machine learning algorithms like Polynomial regression with threshold, Decision Tree, Random Forest ensemble and variants of Deep Neural Networks (DNN) have been employed to analyse the biometric patterns from the experimental data acquired under different circumstances and detect the user’s mood to assign a score to the user. The proposed approach uses a reinforcement learning algorithm to calibrate the user’s current temperament by taking intermediate user feedback and comparing the score with the temperament. The results reveal that the proposed system detects the user’s mood fluctuations with higher accuracy and relevance compared to any contemporary model.