Intelligent Fall Detection and Emergency Response for Smart Homes Using Language Models

Fall incidents are a major concern for vulnerable populations, significantly contributing to annual mortality rates and highlighting the need for effective remote healthcare monitoring and response systems. Despite the introduction of numerous AI-based automatic fall detection methodologies, existing systems lack continuous communication support and often fail to adequately serve vulnerable populations, such as disabled individuals who may be unable to communicate. This study proposes a novel fall detection system for smart homes using small language models (SLMs) to address these limitations. By incorporating real-time conversation support, the system enhances its usefulness for vulnerable populations and improves communication with emergency care teams. We present the system design, integrate lightweight machine learning models, and evaluate its performance using the TinyLlama and Phi-3 models. Our evaluation focuses on accuracy, response time, and relevance of responses, providing insights into the effective implementation of language models in high-reliability systems for smart home healthcare monitoring.