산업현장 고소작업자를 위한 스마트 안전 벨트

• Published in: 한국컴퓨터정보학회논문지 Vol. 23; no. 2; pp. 63 - 70
• Main Authors: 이세훈(Se-Hoon Lee), 문효재(Hyo-Jae Moon), 탁진현(Jin-Hyun Tak)
• Format: Journal Article
• Language: Korean
• Published: 한국컴퓨터정보학회 01.02.2018
• Subjects: 컴퓨터학; Real-time Monitoring; Data collection; Deep-Learning; Worker‘s Safety Management
• ISSN: 1598-849X; 2383-9945
• DOI: 10.9708/jksci.2018.23.02.063

Safety management agent manages the risk behavior of the worker with the naked eye, but there is a real difficulty for one the agent to manage all the workers. In this paper, IoT device is attached to a harness safety belt that a worker wears to solve this problem, and behavior data is upload to the cloud in real time. We analyze the upload data through the deep learning and analyze the risk behavior of the worker. When the analysis result is judged to be dangerous behavior, we designed and implemented a system that informs the manager through monitoring application. In order to confirm that the risk behavior analysis through the deep learning is normally performed, the data values of 4 behaviors (walking, running, standing and sitting) were collected from IMU sensor for 60 minutes and learned through Tensorflow, Inception model. In order to verify the accuracy of the proposed system, we conducted inference experiments five times for each of the four behaviors, and confirmed the accuracy of the inference result to be 96.0%. KCI Citation Count: 0