Development of a Fall Detection and Safety Communication System Using Small Language Models
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| Publicado en: | ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction vol. 42 (2025), p. 588-595 |
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| Autor principal: | |
| Otros Autores: | , , , |
| Publicado: |
IAARC Publications
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| Acceso en línea: | Citation/Abstract Full Text - PDF |
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MARC
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|---|---|---|---|
| 001 | 3240508876 | ||
| 003 | UK-CbPIL | ||
| 035 | |a 3240508876 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 180234 |2 nlm | ||
| 100 | 1 | |a Kankanamge, Malithi Wanniarachchi |u School of Computing, Southern Illinois University, Carbondale, IL, USA | |
| 245 | 1 | |a Development of a Fall Detection and Safety Communication System Using Small Language Models | |
| 260 | |b IAARC Publications |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a 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. | |
| 653 | |a Communications systems | ||
| 653 | |a Fall detection | ||
| 653 | |a System reliability | ||
| 653 | |a Machine learning | ||
| 653 | |a Real time | ||
| 653 | |a Occupational safety | ||
| 653 | |a Construction industry | ||
| 653 | |a Language | ||
| 653 | |a Emergency medical care | ||
| 653 | |a Workers | ||
| 653 | |a Cameras | ||
| 653 | |a Fatalities | ||
| 653 | |a Artificial intelligence | ||
| 653 | |a Teams | ||
| 653 | |a Computer vision | ||
| 653 | |a Verbal communication | ||
| 653 | |a Sensors | ||
| 653 | |a Emergency communications systems | ||
| 653 | |a At risk populations | ||
| 653 | |a Automation | ||
| 653 | |a Surveillance | ||
| 653 | |a Supervisors | ||
| 653 | |a Robotics | ||
| 700 | 1 | |a Shahid, Abdur R |u School of Computing, Southern Illinois University, Carbondale, IL, USA | |
| 700 | 1 | |a Yang, Ning |u School of Computing, Southern Illinois University, Carbondale, IL, USA | |
| 700 | 1 | |a Uddin, S M Jamil |u Department of Construction Management, Colorado State University, USA | |
| 700 | 1 | |a Biswas, Rahul |u Faculty of Engineering, Rangamati Science and Technology University, Bangladesh | |
| 773 | 0 | |t ISARC. Proceedings of the International Symposium on Automation and Robotics in Construction |g vol. 42 (2025), p. 588-595 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3240508876/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3240508876/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |