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

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Pubblicato in:ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction vol. 42 (2025), p. 588-595
Autore principale: Kankanamge, Malithi Wanniarachchi
Altri autori: Shahid, Abdur R, Yang, Ning, Uddin, S M Jamil, Biswas, Rahul
Pubblicazione:
IAARC Publications
Soggetti:
Communications systems
Fall detection
System reliability
Machine learning
Real time
Occupational safety
Construction industry
Language
Emergency medical care
Workers
Cameras
Fatalities
Artificial intelligence
Teams
Computer vision
Verbal communication
Sensors
Emergency communications systems
At risk populations
Automation
Surveillance
Supervisors
Robotics
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Abstract:Fall incidents are a leading cause of injuries and fatalities in the construction industry, significantly impacting worker safety and productivity. Although many Al-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 high-risk 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.
Fonte:Advanced Technologies & Aerospace Database