Wearables, IoT, and Artificial Intelligence for Mental Health Monitoring: A Systematic Review.

Resumen

Digital technologies have begun to represent an important option for improving mental health monitoring, which has become a fundamental challenge worldwide. This study presents a systematic review of the use of wearable sensors, the Internet of Things (IoT), and artificial intelligence (AI) for monitoring and early detection of mental health problems. To this end, 30 studies were analyzed using the PRISMA method and filtered using PICOC criteria. The results show that the most studied disorders are anxiety, stress, and depression. In addition, the most used sensors are those for heart rate, sound, physical activity, and EEG, as they allow for continuous and non-invasive data collection. In terms of AI techniques, most studies use conventional techniques such as SVM, regression, and XGBoost; however, other studies already incorporate more advanced models that combine neural networks and attention mechanisms. These technologies often offer advantages such as early detection, continuous monitoring, and personalized follow-up. However, there are other limitations related to privacy, data variability, and lack of clinical validation. In short, it is concluded that the integration of sensors, AI, and IoT has great potential, but greater standardization and research in diverse populations is required for its real-world application in mental health.