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Development of a methodology to predict and monitor emergency situations of the elderly based on object detection

  • Sekyoung Youm
  • Changgyun Kim
  • Seunghyun Choi
  • Yong-Shin Kang
Article
  • 13 Downloads

Abstract

Because on the increase in the number of the elderly living alone and accidents occurring to them, the demand for a monitoring system capable of supporting fast response in case of an emergency situation by monitoring their everyday life in their residential spaces has been increasing. A framework and a system are presented to monitor the emergency situations of the elderly living alone using a low-cost device and open-source software. First, human pose recognition and emergency situations according to the pose change were defined using object recognition, and a procedure capable of detecting such situations was proposed. In addition, a pose recognition model was created using the TensorFlow Object Detection application programming interface (API) of Google to implement the procedure. Using a data preprocessing process and the created model, a system capable of detecting emergency situations and sounding an alarm was implemented. To verify the proposed system, the pose recognition success rate was examined, and an experiment on emergency situation recognition was performed while the angle and distance of the camera were varied in a setup similar to the residential environment. It is expected that the proposed framework for the emergency notification system for the elderly will be utilized for the analysis of various behavior patterns, such as the sudden abnormal behavior of the elderly, people with disabilities, and children.

Keywords

TensorFlow Pose recognition The elderly Emergency situation recognition Object-detection 

Notes

Acknowledgements

This work was supported by the Ministry of Land, Infrastructure and Transport in Korea.

(17CTAP-C114867-02)

References

  1. 1.
    Allen JG, Xu RYD, Jin JS (2004) Object tracking using camshift algorithm and multiple quantized feature spaces. In: Proceedings of the Pan-Sydney area workshop on Visual information processing. Australian Computer Society, Inc., p. 3–7Google Scholar
  2. 2.
    Anderson D, Luke RH, Keller J, Skubic M, Rantz M, Aud M (2009) Linguistic summarization of activities from video for fall detection using voxel person and fuzzy logic. Comput Vis Image Underst 113(1):80–89CrossRefGoogle Scholar
  3. 3.
    Bourke AK, O’brien JV, Lyons GM (2007) Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture 26(2):194–199CrossRefGoogle Scholar
  4. 4.
    CANNY J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 6:679–698CrossRefGoogle Scholar
  5. 5.
    Cho W-D et al (2014) Life log big data-based lifestyle analysis and wellness prediction using IoT care service system. Journal of The Korean Institute of Communication Sciences 35(12):17–24Google Scholar
  6. 6.
    Choi K-S, Chun J-C (2015) Development of mobility and vitality signal monitoring system based on ZigBee-PSTN gateway for the elderly. The Journal of Korea Institute of Information, Electronics, and Communication Technology 9(1):9–14CrossRefGoogle Scholar
  7. 7.
    Chung W-Y, et al (2008) A wireless sensor network compatible wearable U-healthcare monitoring system using integrated ECG, accelerometer and SpO2 30th annual international IEEE EMBS conference Vancouver, British ColumbiaGoogle Scholar
  8. 8.
    Cutler DM, Poterba JM, Sheiner LM, Summers LH, Akerlof GA (1990) An aging society: opportunity or challenge. Brook Pap Econ Act 1:1–73CrossRefGoogle Scholar
  9. 9.
    Elgammal A et al (2000) Non-parametric model for background subtraction, ECCV 2000, LNCS 1843, pp. 751–767Google Scholar
  10. 10.
    Fougère M, Mérette M (1999) Population ageing and economic growth in seven OECD countries. Econ Model 16(3):411–427CrossRefGoogle Scholar
  11. 11.
    Girshick R (2015) Fast r-cnn arXiv preprint arXiv:1504.08083Google Scholar
  12. 12.
    Gkioxari G, Girshick R, Malik J (2015) Contextual action recognition with r-cnn. In Proceedings of the IEEE international conference on computer vision, pp 1080–1088Google Scholar
  13. 13.
    Jang I-H et al (2007) Ring-type Heart Rate Sensor and Monitoring system for Sensor Network Application, Journal of Korean institute of intelligent systems, Vol.17 No.5Google Scholar
  14. 14.
    Kim N (2010) Development of an emergency monitoring device in a wrist watch. Communications of the Korean Institute of Information Scientists and Engineers 8(4)Google Scholar
  15. 15.
    Kim TH, Ko ZK (2013) Problems and improvement devices of normative system on welfare of the aged act according to the aging society. Inha Law Review The Institute of Legal Studies Inha University 16(1):167–198Google Scholar
  16. 16.
    Kim YS, Lee CM, Namgung SJ, Kim HG (2011) A study on the social networks effectiveness to prevent the lonely death of the elderly who live alone. Soc Sci Res 50(2):143–169Google Scholar
  17. 17.
    Lee TK et al. (2016) A basic study on human injuries according to slip velocity during falling. Proceedings of the Korean Society of Precision Engineering Conference, pp. 217–218Google Scholar
  18. 18.
    Mastorakis G, Makris D (2012) Fall detection system using Kinect’s infrared sensor. Journal of Real-Time Image ProcessingGoogle Scholar
  19. 19.
    Mehta R, Kale S, Utage AS (2017) The internet of things (IOT) intelligence computing Technology for Home Automation. International Journal of Current Engineering and TechnologyGoogle Scholar
  20. 20.
    Mingwu R, Han S (2005) A practical method for moving target detection under complex background. Computer Engineering, pp. 33–34Google Scholar
  21. 21.
    Otto C et al (2006) System architecture of a wireless body area sensor network for ubiquitous health monitoring. Journal of Mobile Multimedia 1(4):307–326Google Scholar
  22. 22.
    Paradiso R et al (2005) WEALTHY – a wearable healthcare system: new frontier on e-textile, Journal of Telecommunications and Information Technology, 4/2005Google Scholar
  23. 23.
    Pfister T, Charles J, Zisserman A (2015) Flowing convnets for human pose estimation in videos. In: Proceedings of the IEEE International Conference on Computer Vision, p. 1913–1921Google Scholar
  24. 24.
    Planinc R, Kampel M (2012) Robust fall detection by combining 3D data and fuzzy logic. ACCV Workshop on Color Depth Fusion in Computer Vision, pp. 121–13CrossRefGoogle Scholar
  25. 25.
    Rogez G, Weinzaepfel P, Schmid C (2018) LCR-net++: multi-person 2D and 3D pose detection in natural images. arXiv preprint arXiv:1803.00455Google Scholar
  26. 26.
    Rougier C, Anvient E, Rousseau J, Mignotte M, Meunier J (2011) Fall detection from depth map video sequences,” Intl Conf on Smart Homes and Health Telematics, pp. 121–128Google Scholar
  27. 27.
    Schneider S, Taylor GW, Kremer SC (2018) Deep Learning Object Detection Methods for Ecological Camera Trap Data arXiv preprint arXiv 1803.10842Google Scholar
  28. 28.
    Sonka M, Hlavac V, Boyle R (2003) Image processing, analysis, and machine vision (second edition). Posts & Telecom Press, BeijingGoogle Scholar
  29. 29.
    Stone EE, Skubic M (2015) Fall detection in homes of older adults using the Microsoft Kinect. IEEE Journal of Biomedical and Health Informatics 19(1):290–301CrossRefGoogle Scholar
  30. 30.
    Yang Y (2013) Articulated human pose estimation with flexible mixtures of parts. University of California, IrvineGoogle Scholar
  31. 31.
    Yunchu Z, Zize L, En L, Min T (2006) A Background Reconstruction Algorithm Based on C-means Clustering for Video Surveillance. Computer Engineering and Application, pp. 45–47Google Scholar
  32. 32.
    Zeng A, Yu KT, Song S, Suo D, Walker E, Rodriguez A, Xiao J (2017) Multi-view self-supervised deep learning for 6d pose estimation in the amazon picking challenge. In Robotics and Automation (ICRA), 2017 IEEE International Conference on, pp 1386–1383Google Scholar
  33. 33.
    Zhan C et al (2007) An improved moving object detection algorithm based on frame difference and edge detection. In: image and graphics, 2007. ICIG 2007. Fourth international conference on. IEEE, p. 519–523Google Scholar
  34. 34.
    Zhu L, Wang R, Wang Z, Yang H (2017) TagCare: using RFIDs to monitor the status of the elderly living alone. IEEE Access 5:11364–11373CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Sekyoung Youm
    • 1
  • Changgyun Kim
    • 1
  • Seunghyun Choi
    • 1
  • Yong-Shin Kang
    • 2
  1. 1.Department of Industrial and System EngineeringDongguk UniversitySeoulSouth Korea
  2. 2.Department of Systems Management EngineeringSungkyunkwan UniversitySuwonSouth Korea

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