TRANSHEALTH: A Transformer-BDI Hybrid Framework for Real-Time Psychological Distress Detection in Ambient Healthcare

Psychological distress detection plays a critical role in modern healthcare, especially in ambient environments where continuous monitoring is essential for timely intervention. Advances in sensor technology and artificial intelligence (AI) have enabled the development of systems capable of mental health monitoring using multi-modal data. However, existing models often struggle with contextual adaptation and real-time decision-making in dynamic settings. This paper addresses these challenges by proposing TRANS-HEALTH, a hybrid framework that integrates transformer-based inference with Belief-Desire-Intention (BDI) reasoning for real-time psychological distress detection. The framework utilizes a multimodal dataset containing EEG, GSR, heart rate, and activity data to predict distress while adapting to individual contexts. The methodology combines deep learning for robust pattern recognition and symbolic BDI reasoning to enable adaptive decision-making. The novelty of the approach lies in its seamless integration of transformer models with BDI reasoning, providing both high accuracy and contextual relevance in real time. Performance metrics such as accuracy, precision, recall, and F1-score are employed to evaluate the system’s performance. The results show that TRANS-HEALTH outperforms existing models, achieving 96.1% accuracy with 4.78 ms latency and significantly reducing false alerts, with an enhanced ability to engage users, making it suitable for deployment in wearable and remote healthcare environments.