Development of a Fall Detection and Safety Communication System Using Small Language Models

Fall incidents are a leading cause of injuries and fatalities in the construction industry, significantly impacting worker safety and productivity.Although many AI-based automated fall detection methods have been introduced, existing systems lack continuous communication support and often fail to address critical scenarios such as isolated work zones or highrisk tasks involving limited oversights.To address these limitations, this study proposes a new fall detection system for the construction industry using small language models (SLMs).Incorporating real-time conversation support within the system improves communication with emergency care teams and increases its utility in high-risk environments.We present the system architecture, integrate lightweight machine learning models, and evaluate the system's performance using the TinyLlama and Phi-3 models.Our assessment, which emphasizes relevance, correctness, and response speed, provides essential insights into effectively integrating language models into high-reliability systems for the construction industry.