Advancing Stress Detection and Health Monitoring with Deep Learning Approaches

Abstract

Numerous studies in the healthcare field conducted in recent years have highlighted the impact of stress on health and its role in the development of several critical illnesses. Stress monitoring using wearable technologies, such as smartwatches and biosensors, has shown promising results in improving the management of this issue. Data from both physical and mental health can be leveraged to enhance medical decision-making, support research on new treatments, and deepen our understanding of complex diseases. However, traditional machine learning (ML) systems often face limitations, particularly in real-time processing and resource optimization, which restrict their application in critical situations. In this article, we present the development of a deep learning-based approach that leverages models such as 1D CNN (Convolutional Neural Networks), LSTM (Long Short-Term Memory), and Time-Series Transformers, alongside classical deep learning techniques. We then highlight the transformative potential of TinyML for real-time, low-power health monitoring, focusing on Heart Rate Variability (HRV) analysis. This approach aims to optimize personalized health interventions and enhance the accuracy of medical monitoring.