Stress Level

Stress level research focuses on accurately detecting and managing stress, aiming to improve mental and physical well-being. Current research employs multimodal machine learning approaches, often integrating physiological signals (ECG, EDA, HRV) with behavioral data (facial expressions, posture, activity levels) and leveraging algorithms like gradient boosting and anomaly detection models to achieve high accuracy in stress classification. These advancements offer potential for personalized stress management interventions and improved diagnostic tools, particularly in areas like neurodegenerative disease monitoring and workplace wellness. The development of non-invasive, seamless monitoring systems is a key area of ongoing development.