Continuous Chronic Stress

Continuous chronic stress research aims to understand the pervasive effects of long-term stress on individuals and develop effective detection and mitigation strategies. Current research focuses on leveraging machine learning, particularly neural networks (including CNNs, LSTMs, and transformers), to analyze diverse data sources like physiological signals (ECG, EDA, PPG), voice patterns, and social media text to identify and predict stress levels, often employing techniques like multi-task learning and anomaly detection. These advancements hold significant promise for improving mental health assessments, personalized interventions, and the development of more robust AI systems capable of handling stress-inducing situations.