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Active fall prevention: robotic vision in elderly care

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Effective methods of preventing falls significantly improve the quality of life of the Elderly. Nowadays, people focus mainly on the proper provision of the apartment with handrails and fall detection systems once they have occurred. The article presents a system of active detection and classification of the risk of falls in the home space using a service robot equipped with a vision sensor. The fusion of image-based and depth-based processing paths allows for the effective object detection. Hazard classification allows for executing the tasks assigned to the robot while maintaining a high level of user safety.
Rocznik
Strony
27--38
Opis fizyczny
Bibliogr. 23 poz., rys., tab.
Twórcy
  • Warsaw University of Technology, Institute of Control and Computation Engineering, Warsaw, Poland
  • Warsaw University of Technology, Institute of Control and Computation Engineering, Warsaw, Poland
Bibliografia
  • [1] EuroSafe, “Injuries in the European Union. Summary of injuries statistics for the years 2012-2014.”
  • [2] R. Halik, J. Stokwiszewski, B. Wojtyniak, and W. Seroka, Injuries to people over 60 years old in Poland. National Institute of Public Health - National Institute of Hygiene, 2018, (report in Polish).
  • [3] OECD, “Population projections,” in Demography and population. OECD Publishing, 2020, https://stats.oecd.org/Index.aspx?DataSetCode=POP_PROJ#, (accessed 5 march 2020).
  • [4] L. D. Gillespie, M. C. Robertson, W. J. Gillespie, C. Sherrington, S. Gates, L. M. Clemson, and S. E. Lamb, “Interventions for preventing falls in older people living in the community,” Cochrane database of systematic reviews, no. 9, 2012.
  • [5] EU FP7 grant, “Squirrel: Clearing clutter bit by bit,” 2014-2018. [Online]. Available: http://www.squirrel-project.eu
  • [6] Preferred Networks Inc., “Autonomous tidying-up robot system,” CEATEC JAPAN 2018. [Online]. Available: https://projects.preferred.jp/tidying-up-robot/en/
  • [7] M. Bajones, D. Fischinger, A.Weiss, D.Wolf, M. Vincze, P. de la Puente, T. Kortner, M. Weninger, K. Papoutsakis, D. Michel et al., “Hobbit: Providing fall detection and prevention for the elderly in the real world,” Journal of Robotics, vol. 2018, 2018.
  • [8] W. Dudek, M.Wegierek, J. Karwowski, W. Szynkiewicz, and T. Winiarski, “Task harmonisation for a single–task robot controller,” in 12th International Workshop on Robot Motion and Control (RoMoCo). IEEE, 2019, pp. 86–91.
  • [9] Z.-Q. Zhao, P. Zheng, S.-t. Xu, and X. Wu, “Object detection with deep learning: A review,” IEEE transactions on neural networks and learning systems, vol. 30, no. 11, pp. 3212–3232, 2019.
  • [10] T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollar, and C. L. Zitnick, “Microsoft coco: Common objects in context,” in European conference on computer vision. Springer, 2014, pp. 740–755.
  • [11] R. B. Rusu and S. Cousins, “3d is here: Point cloud library (pcl),” in 2011 IEEE international conference on robotics and automation. IEEE, 2011, pp. 1–4.
  • [12] D. Holz and S. Behnke, “Fast range image segmentation and smoothing using approximate surface reconstruction and region growing,” in Intelligent autonomous systems 12. Springer, 2013, pp. 61–73.
  • [13] M. Stefanczyk and W. Kasprzak, “Multimodal segmentation of dense depth maps and associated color information,” in International Conference on Computer Vision and Graphics. Springer, 2012, pp. 626–632.
  • [14] A. Ecins, C. Fermuller, and Y. Aloimonos, “Cluttered scene segmentation using the symmetry constraint,” in 2016 IEEE International Conference on Robotics and Automation (ICRA). IEEE, 2016, pp. 2271–2278.
  • [15] A. Canziani, A. Paszke, and E. Culurciello, “An analysis of deep neural network models for practical applications,” arXiv preprint arXiv:1605.07678, 2016.
  • [16] C. Zieli´nski, M. Stefa´nczyk, T. Kornuta, M. Figat, W. Dudek, W. Szynkiewicz, W. Kasprzak, J. Figat, M. Szlenk, T. Winiarski, K. Banachowicz, T. Zielińska, E. G. Tsardoulias, A. L. Symeonidis, F. E. Psomopoulos, A. M. Kintsakis, P. A. Mitkas, A. Thallas, S. E. Reppou, G. T. Karagiannis, K. Panayiotou, V. Prunet, M. Serrano, J.-P. Merlet, S. Arampatzis, A. Giokas, L. Penteridis, I. Trochidis, D. Daney, and M. Iturburu, “Variable structure robot control systems: The rapp approach,” Robotics and Autonomous Systems, vol. 94, pp. 226 – 244, 2017.
  • [17] T. Winiarski, W. Dudek, M. Stefanczyk, Ł. Zielinski, D. Giełdowski, and D. Seredynski, “An intent-based approach for creating assistive robots’ control systems,” arXiv preprint arXiv:2005.12106, 2020. [Online]. Available: http://arxiv.org/abs/2005.12106
  • [18] D. Seredynski, K. Banachowicz, and T. Winiarski, “Graph–based potential field for the end–effector control within the torque–based task hierarchy,” in 21th IEEE International Conference on Methods and Models in Automation and Robotics, MMAR’2016. IEEE, 2016, pp. 645–650.
  • [19] S. Krivic and J. Piater, “Online adaptation of robot pushing control to object properties,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2018, pp. 4614–4621.
  • [20] W. Dudek, K. Banachowicz, W. Szynkiewicz, and T. Winiarski, “Distributed NAO robot navigation system in the hazard detection application,” in 21th IEEE International Conference on Methods and Models in Automation and Robotics, MMAR’2016. IEEE, 2016, pp. 942–947.
  • [21] “INCARE project page at WUT,” Mar. 2020. [Online]. Available: https://www.robotyka.ia.pw.edu.pl/projects/incare/
  • [22] J. Kolakowski, V. Djaja-Josko, M. Kolakowski, and J. Cichocki, “Localization system supporting people with cognitive impairment and their caregivers,” International Journal of Electronics and Telecommunications, vol. 66, no. 1, pp. 125–131, 2020.
  • [23] A.-V. Sitar-Taut, D.-A. Sitar-Taut, O. Cramariuc, V. Negrean, D. Sampelean, L. Rusu, O. Orasan, A. Fodor, G. Dogaru, and A. Cozma, “Smart homes for older people involved in rehabilitation activities-reality or dream, acceptance or rejection,” Balneo Res J, vol. 9, no. 3, pp. 291–8, 2018.
Uwagi
PL
Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-e79301c1-2ff0-4fc9-8a1f-14fad8782f00
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