Abstract:In today’s fast-paced world, stress has become an almost unavoidable part of daily life, and when left unchecked, it can lead to serious mental and physical health issues. Unfortunately, recognizing and managing stress in real time is still a major challenge, especially when traditional tools like self-reporting and periodic check-ins can miss critical moments. This research explores a new and more responsive approach: using nanosensor technology to track stress as it happens, directly through the body’s own chemical signals. By detecting psychophysiological stress biomarkers such as cortisol, adrenaline, dopamine, and serotonin in real-time, wearable nanosensors offer a powerful tool for mental health monitoring. These ultra-sensitive devices can analyze tiny changes in sweat, saliva, or even the air around your skin, giving a real-time window into how your body is responding to emotional strain. For example, elevated cortisol and adrenaline levels are closely linked to acute stress, while drops in serotonin and dopamine often signal anxiety or depression. This study focuses on the design and integration of flexible, skin-friendly nanosensors that continuously monitor these biomarkers and wirelessly send data to a smartphone or digital platform. What sets this work apart is the review of the integration of artificial intelligence (AI), which plays a pivotal role in processing complex sensor data, identifying stress patterns, and enabling personalized health insights. Machine learning algorithms can analyze fluctuations in biomarker levels over time, making it possible to predict mental health trends and trigger alerts before symptoms become severe. We also address challenges like individual variability in biomarker expression and sensor calibration, aiming to make the system both accurate and adaptable. By translating biochemical signals into meaningful insights, nanosensors could empower individuals to understand and manage stress more effectively, leading to healthier minds and better overall well-being.
Keywords: Mental Health, Nanosensors, Stress, Biomarkers, Real-Time Monitoring, Mental Health
