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- Title
Detection of Fall-Related Accidents Using Deep Learning Method in the Internet of Things.
- Authors
Aksoy, Bekir; Salman, Osamah Khaled Musleh; Sayın, Hamdi; Sayın, İrem
- Abstract
Nowadays, with the increase in the number of employees in the enterprises and the proportional workload, different occupational accidents frequently occur. Examples of this are slippery floors, falling materials, harmful substances/gas leaks, improper use of protective clothing and equipment or not using them at all. Identifying these dangers and taking the necessary precautions are very important for both worker safety and employers. The most common accident among these dangerous situations is the accidents that occur as a result of slipping or falling. Such accidents are usually caused by a foreign liquid/substance on the work surface, the worker's inability to balance himself, or surface inequalities. With this study, an IoT and 1D CNN deep learning-based system has been developed to detect accidents such as falling, slipping and balance disorders to inform relevant health institutions. The developed 1D CNN-based system detected work accidents caused by falls with 100% accuracy. With the results obtained from this study, it is aimed to make improvements for the prevention of these accidents.
- Subjects
INTERNET of things; DEEP learning; WORK-related injuries; PROTECTIVE clothing; ARTIFICIAL intelligence
- Publication
Gazi Journal of Engineering Sciences (GJES) / Gazi Mühendislik Bilimleri Dergisi, 2022, Vol 8, Issue 2, p189
- ISSN
2149-4916
- Publication type
Article
- DOI
10.30855/gmbd.0705003