Emotion-aware wearable devices integrating biosignals for mental health monitoring using random forest algorithm

Abstract

The health sector is undergoing a transformation with the advancement of technology. This research presents a comprehensive emotion aware mental health monitoring system which uses random forest algorithm for emotion classification. By leveraging the Random Forest algorithm, the system can monitor and tell if one is going through depression, anxiety or any other mental instability based on HRV, temperature and EDA. Mental health issues are a growing concern worldwide with limited access to timely diagnosis and intervention. In Zimbabwe there is a significant rise in mental health issues closely linked to increasing substance abuse particularly among youths using drugs such as Dombo (marijuana). Wearable devices are capable of continuously monitoring physiological biosignals and present a solution for real time emotion detection and mental health monitoring. This research explores the integration of biosignals acquisition from Empatica EmbracePlus smart watch with artificial intelligence algorithm such as random forest for emotion classification using APIs, enabling emotion-aware monitoring. By continuously capturing physiological signals linked to emotional wellbeing, these devices offer an objective, scalable means of detecting early signs of emotional distress or mental health deterioration.