한국컴퓨터정보학회논문지 (Journal of the Korea Society of Computer and Information)

  • 제23권2호
  • /
  • Pages.63-70
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  • 2018
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  • 1598-849X(pISSN)
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  • 2383-9945(eISSN)
한국컴퓨터정보학회 (Korean Society of Computer Information)
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Smart Safety Belt for High Rise Worker at Industrial Field

  • Lee, Se-Hoon (Dept. of Computer Systems & Engineering, Inha Technical College) ;
  • Moon, Hyo-Jae (Dept. of Computer Systems & Engineering, Inha Technical College) ;
  • Tak, Jin-Hyun (R&D Center, DucSan Information Telecom Co., Ltd.)
  • 투고 : 2018.01.29
  • 심사 : 2018.02.27
  • 발행 : 2018.02.28
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초록

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%.

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참고문헌

  1. Korea Occupational Safety & Health Agency, "Industrial Accident Analysis", 2016
  2. J. W. Jeong, "Theory and Real of Industrial Safety and Health Management", 2015
  3. K.T. Kim. "A study on the Implementation of USN Technologies for Safety Management Monitoring of Architectural Construction Sites", J, Korea Inst. Building Construction. Vol. 9, No. 4, pp. 103-109 Aug. 2009 https://doi.org/10.5345/JKIC.2009.9.4.103
  4. Korea Occupational Safety & Health Agency, "Manpower transport safety", 2008
  5. Korea Occupational Safety & Health Agency, "Standard Work Safety (Manufacturing)", 2009
  6. S. G. Hong, (2017) "Gait feature extraction algorithm based on useractivity recognition using inertial sensors", Master's Thesis, University of Science and Technology, Daejeon
  7. Nils Y. Hammerla, S. Halloran & T. Plotz, (2016) "Deep, Convolutional, and Recurrent Modelsfor Human Activity Recognition Using Wearables", Proceedings. the Twenty-Fifth International Joint Conference on Artificial Intelligence, 1533-1540
  8. Geun Chul Park, A. Y. Jeon, S. H. Lee at al, (2013) "Implementation of a Falls Recognition System Using Acceleration and Angular Velocity Signals", Journal of Sensor Science and Technology, Vol.22 No.1, 54-65 https://doi.org/10.5369/JSST.2013.22.1.54
  9. K. Y. Lee (2012), An Algorithm of Indoor Pedestrian Dead-Reckoning using IMU Sensor and Map Matching, Master's Thesis, Kangwon National University, Kangwon
  10. S.J. Jeong, J.H. Lim (2015) "Implementation of USN based Personal Safety Belt Monitoring System", ournal of the Korea Institute of Information and Communication Engineering, Vol.19 No.3, 724-730 https://doi.org/10.6109/jkiice.2015.19.3.724
  11. B. G. Ahn , Y. H. Noh ,Jeong & D. U. Jeong (2015), "Implementation of Behavior Notification System for Guide Dog Harness Using IMU and Accelerometer Sensor", The Journal of Korea Institute of Signal Processing and Systems, Vol.16 No.1, 15-21
  12. R. Grzeszick, J.M. Lenk, & F.M. Rueda[1 at el, (2017). Deep Neural Network based Human Activity Recognition for the Order Picking Process. Proceedings Of The 4Th International Workshop On Sensor-Based Activity Recognition And Interaction - Iwoar '17. http://dx.doi.org/10.1145/3134230.3134231
  13. Dehzangi, O., Taherisadr, M., & ChangalVala, R. (2017). IMU-Based Gait Recognition Using Convolutional Neural Networks and Multi-Sensor Fusion. Sensors, 17(12), 2735. http://dx.doi.org/10.3390/s17122735
  14. Woo-Young Kang, B.H. Kim & B.T.Zhang (2016) "Hangeul Handwriting Recognition using Deeper Convolutional Neural Networks Based on Inception Modules",KCC, Vol.2016 No.6, 883-885
  15. Christian Szegedy, W. Liu, Y. Jia et al, "Going deeper with convolutions", Retrieved from https://arxiv.org/pdf/1409.4842.pdf
  16. Jurgen Schmidhuber (2014), "Deep Learning in Neural Networks: An Overview" http://arxiv.org/abs/1404.7828
  17. Choi, Hyoungin (2016), "A Study of Fall Detection before Impact Point usingan IMU Sensor",Master's Thesis, Korea University of Technology Education