AIoT for human emotion recognition: Potentials, challenges, and healthcare applications

Emotions are critical human behavior and cognition drivers, influencing communication, decision-making, and well-being. Emotion recognition (ER) is the computational identification of emotional states, and it has therefore gained considerable attention across various fields, including human-computer interaction, mental health, and intelligent systems. This review article synthesizes recent advancements in ER enabled by the integration of the Internet of Things (IoT) and Artificial Intelligence (AI), collectively termed AIoT, with a specific focus on healthcare applications. We highlight IoT-based sensing technologies, including wearables, ambient sensors, and mobile devices, which enable continuous and non-intrusive monitoring of emotions through multimodal signals such as facial expressions, speech, EEG, ECG, and GSR. A comprehensive taxonomy is proposed that organizes sensing modalities, datasets, pre-processing methods, learning algorithms, and application domains. Both traditional machine learning methods (e.g., SVM, Random Forests) and modern deep learning approaches (e.g., CNNs, LSTMs, Transformers) are evaluated for their ability to effectively handle complex emotional data. The integration of AI and IoT is presented as essential for developing scalable, real-time, and context-sensitive emotion-aware systems for healthcare applications. We discuss key challenges such as data heterogeneity, privacy, interpretability, and limited labeled datasets along with future directions such as edge computing, federated learning, and explainable AI. This synthesis aims to guide the development of robust, personalized, AIoT-enabled emotion-aware healthcare systems.