Large Language Model‐Driven Throat‐Wearable Sensing System for Real‐Time Recognition and Evaluation of Swallowing Disorders

ABSTRACT

Swallowing disorders are a common complication after stroke, yet current assessment methods rely largely on clinical observation and subjective screening, limiting continuous and objective evaluation. Here, we report a large language model (LLM)‐driven throat‐wearable sensing system (TWSS) for real‐time monitoring of laryngeal activity and quantitative assessment of swallowing function. TWSS consists of a flexible sensing patch and a structured signal sequence–based LLM framework (S3‐LLM). The flexible patch integrates a stretchable sensor with a wireless circuit module, enabling conformal attachment to the throat and real‐time acquisition of physiological signals. Owing to its dual sensitivity to pressure and strain, the sensor can capture subtle and complex laryngeal movements associated with different physiological activities. S3‐LLM converts them into structured signal sequences and combines temporal encoding with parameter‐efficient fine‐tuning, thereby exploiting the representation and generalization capabilities of LLMs under few‐shot conditions. In a clinical validation involving 20 participants, TWSS achieved an accuracy of 92.4% for recognizing normal laryngeal activities and 87.6% for evaluating swallowing function, approximately 20% improvement over conventional models. These results demonstrate that TWSS provides a promising wearable platform for continuous, objective, and quantitative assessment of swallowing disorders and highlights the potential for personalized healthcare.