AI Driven Emotion Recognition: Leveraging IoT Sensors and Machine Learning for Early Detection of Psychological Disorders

Abstract

The rapid development of Artificial Intelligence (AI) and Machine Learning (ML) approaches has significantly improved the mental health surveillance systems, particularly through emotion recognition. Facial expression recognition, a crucial part of affective computing, has been identified as a reliable method of assessing emotional conditions, which are crucial indicators of Psychological Disorders. ML could offer real-time and non-invasive surveillance solutions. This study analyzes AI and the Internet of Things (IoT) use in facial recognition to identify emotions and their role in enhancing mental health surveillance systems. The study highlights the importance of leveraging large annotated datasets and the roles of the Convolutional Neural Networks (CNNs) to increase detectability. The face recognition challenges, such as changing lighting conditions, facial obstruction, and cultural differences in expressing emotions, and the methods provided by AI algorithms to overcome them are also discussed—psychological Disorders. Emotion detection methods can improve the detection of the disorder early, provide custom-made treatment plans using the IoT sensor, and enable continuous monitoring of a patient's psychological well-being.