Real-Time Mouth State Detection Based on a BiGRU-CLPSO Hybrid Model with Facial Landmark Detection for Healthcare Monitoring Applications

The global population is rapidly expanding, driving an increasing demand for intelligent healthcare systems. Artificial intelligence (AI) applications in remote patient monitoring and diagnosis have achieved remarkable progress and are emerging as a major development trend. Among these applications, mouth motion tracking and mouth-state detection represent an important direction, providing valuable support for diagnosing neuromuscular disorders such as dysphagia, Bell’s palsy, and Parkinson’s disease. In this study, we focus on developing a real-time system capable of monitoring and detecting mouth state that can be efficiently deployed on edge devices. The proposed system integrates the Facial Landmark Detection technique with an optimized model combining a Bidirectional Gated Recurrent Unit (BiGRU) and Comprehensive Learning Particle Swarm Optimization (CLPSO). We conducted a comprehensive comparison and evaluation of the proposed model against several traditional models using multiple performance metrics, including accuracy, precision, recall, F1-score, cosine similarity, ROC–AUC, and the precision–recall curve. The proposed method achieved an impressive accuracy of 96.57% with an excellent precision of 98.25% on our self-collected dataset, outperforming traditional models and related works in the same field. These findings highlight the potential of the proposed approach for implementation in real-time patient monitoring systems, contributing to improved diagnostic accuracy and supporting healthcare professionals in patient treatment and care.