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Eklem tabanlı etkili düşme tespiti

Yıl 2017, , 1025 - 1034, 08.12.2017
https://doi.org/10.17341/gazimmfd.369347

Öz

Düşme
yaşlılar için ölüm ve yaralanmalarda en önemli nedenlerden biridir. Gerçek
zamanlı düşme tespiti yaşlıların güvenliği için büyük önem taşımaktadır. Bu
çalışmada, düşme tespiti için iskelet eklem verilerine dayalı yeni bir yöntem önerilmiştir.
21 deneğin katılımı ile oluşturulan FUKinect-Fall veri setindeki üç boyutlu (3b)
iskelet verileri önce iki adet (xy ve zy) iki boyut (2b) eklem verilerine
indirgenmiştir. Daha sonra seçilen referans ekleme göre iç içe geçmiş daireler
üzerine kodlanmış bölgeler oluşturularak her bir eksende kalan 19 eklemin bir
eylem süresince bulunduğu bölge ortalamalarını içeren özellik matrisi
çıkartılmıştır. Bu özellik matrisi k-En Yakın Komşu (k-EYK) ve Destek Vektör
Makinası (DVM) ile sınıflandırılmıştır. Yapılan deneysel çalışmalarda %97,08
doğrulukta düşme tespiti yapılmıştır.

Kaynakça

  • 1. United Nations World Population Ageing 2013. Department of Economic and Social Affairs Population Division.http://www.un.org/en/development/desa/population/publications/pdf/ageing/WorldPopulationAgeing2013pdf. Yayın tarihi 2013. Erişim tarihi Ocak 5, 2015.
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  • 3. Fletcher P.C., Hirdes J.P., Risk factors for falling among community-based seniors using home care services, The Journals of Gerontology Series A: Biological Sciences and Medical Sciences, 57 (8), 504-510, 2002.
  • 4. Tinetti M.E., Speechley M., Ginter S.F., Risk factors for falls among elderly persons living in the community, New England journal of medicine, 319 (26), 1701-1707, 1998.
  • 5. Jensen J., Lundin-Olsson L., Nyberg L., Gustafson Y., Falls among frail older people in residential care, Scandinavian Journal of Public Health, 30 (1), 54-61, 2002.
  • 6. Stevens J.A., Rudd R.A., Circumstances and contributing causes of fall deaths among persons aged 65 and older, Journal of the American Geriatrics Society, 62 (3), 470-475, 2014.
  • 7. Wang R.D., Zhang Y.L., Dong L.P., Lu J.W., Zhang Z.Q., He X., Fall detection algorithm for the elderly based on human characteristic matrix and SVM, In Control Automation and Systems (ICCAS), 15th International Conference, Busan-Korea, 1190-1195, 13-16 October, 2015.
  • 8. Popoola O.P., Wang K, Video-based abnormal human behavior recognition-A review, IEEE Transactions on Systems Man and Cybernetics Part C Applications and Reviews, 42 (6), 865-878, 2012.
  • 9. Yu H., Zheng X., Zhang L., Cao Y., Elderly fall monitoring and remote assistance system, Jisuanji Gongcheng yu Yingyong (Computer Engineering and Applications), 47 (35), 245-248, 2011.
  • 10. Alwan M., Rajendran P.J., Kell S., Mack D., Dalal S., Wolfe M., Felder R., A smart and passive floor-vibration based fall detector for elderly, In Information and Communication Technologies, Damascus-Syria, 1003-1007, 24-26 April, 2006.
  • 11. Rougier C., Meunier J., St-Arnaud A., Rousseau J., Robust video surveillance for fall detection based on human shape deformation, IEEE Transactions on Circuits and Systems for Video Technology, 21 (5), 611-622, 2011. 12. Stone E.E., Skubic M., Fall detection in homes of older adults using the microsoft kinect, IEEE Journal of Biomedical and Health Informatics, 19 (1), 290-301, 2015.
  • 13. Feng W., Liu R., Zhu M., Fall detection for elderly person care in a vision-based home surveillance environment using a monocular camera, Signal Image and Video Processing, 8 (6), 1129-1138, 2014.
  • 14. Bian Z.P., Chau L.P., Magnenat-Thalmann N., Fall detection based on skeleton extraction, Virtual-Reality Continuum and its Applications in Industry, Singapore- Singapore, 91-94, 2-4 December, 2012.
  • 15. Akagündüz E., Aslan M., Şengür A., Wang H., İnce M.C., Silhouette orientation volumes for efficient fall detection in depth videos, IEEE journal of biomedical and health informatics, 21 (3), 756-763, 2017.
  • 16. Ma X., Wang H., Xue B., Zhou M., Ji B., Li Y., Depth-based human fall detection via shape features and improved extreme learning machine, IEEE Journal of Biomedical and Health Informatics, 18 (6), 1915-1922, 2014.
  • 17. Aslan M., Sengur A., Xiao Y., Wang H., Ince M.C., Ma X., Shape feature encoding via Fisher Vector for efficient fall detection in depth-videos, Applied Soft Computing, 37, 1023-1028, 2015.
  • 18. Kepski M., Kwolek B., Fall detection using ceiling-mounted 3d depth camera, 9th International conference on computer vision and applications (VISAPP), Lisbon-Portugal, 5-8 January, 640-647, 2014.
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  • 21. Akbulut Y., Aslan M., Sengur A., Ince M.C., Fall Detection with Kinect-Based Skeleton Data, International Conference on Natural Science and Engineering (ICNASE'16), Kilis-Turkey, 131-139, 24-28 May, 2016.
  • 22. Abhijit Jana. Kinect for Windows SDK Programming Guide. https://www.pdfdrive.net/kinect-for-windows-sdk-programming-guide-pdf-e9001088.html. Yayın tarihi Aralık, 2012. Erişim tarihi Ocak 20,2015.
  • 23. Xia L., Chen C.C., Aggarwal J.K., View invariant human action recognition using histograms of 3d joints, 2012 IEEE Computer Society Conference on In Computer Vision and Pattern Recognition Workshops (CVPRW), Rhode Isaland-USA, 20-27, 16-21 June, 2012.
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  • 31. Xia L., Aggarwal J.K., Spatio-temporal depth cuboid similarity feature for activity recognition using depth camera, In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland Oregon-USA, 2834-2841, 23-28 June, 2013.
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  • 33. Yang X., Tian Y., Super normal vector for activity recognition using depth sequences. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Columbus OH-USA, 804-811, 23-28 June, 2014.
Toplam 32 adet kaynakça vardır.

Ayrıntılar

Bölüm Makaleler
Yazarlar

Muzaffer Aslan 0000-0002-2418-9472

Yaman Akbulut

Abdulkadir Şengür

Melih Cevdet İnce Bu kişi benim

Yayımlanma Tarihi 8 Aralık 2017
Gönderilme Tarihi 8 Mart 2016
Kabul Tarihi 2 Temmuz 2017
Yayımlandığı Sayı Yıl 2017

Kaynak Göster

APA Aslan, M., Akbulut, Y., Şengür, A., İnce, M. C. (2017). Eklem tabanlı etkili düşme tespiti. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 32(4), 1025-1034. https://doi.org/10.17341/gazimmfd.369347
AMA Aslan M, Akbulut Y, Şengür A, İnce MC. Eklem tabanlı etkili düşme tespiti. GUMMFD. Aralık 2017;32(4):1025-1034. doi:10.17341/gazimmfd.369347
Chicago Aslan, Muzaffer, Yaman Akbulut, Abdulkadir Şengür, ve Melih Cevdet İnce. “Eklem Tabanlı Etkili düşme Tespiti”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 32, sy. 4 (Aralık 2017): 1025-34. https://doi.org/10.17341/gazimmfd.369347.
EndNote Aslan M, Akbulut Y, Şengür A, İnce MC (01 Aralık 2017) Eklem tabanlı etkili düşme tespiti. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 32 4 1025–1034.
IEEE M. Aslan, Y. Akbulut, A. Şengür, ve M. C. İnce, “Eklem tabanlı etkili düşme tespiti”, GUMMFD, c. 32, sy. 4, ss. 1025–1034, 2017, doi: 10.17341/gazimmfd.369347.
ISNAD Aslan, Muzaffer vd. “Eklem Tabanlı Etkili düşme Tespiti”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 32/4 (Aralık 2017), 1025-1034. https://doi.org/10.17341/gazimmfd.369347.
JAMA Aslan M, Akbulut Y, Şengür A, İnce MC. Eklem tabanlı etkili düşme tespiti. GUMMFD. 2017;32:1025–1034.
MLA Aslan, Muzaffer vd. “Eklem Tabanlı Etkili düşme Tespiti”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, c. 32, sy. 4, 2017, ss. 1025-34, doi:10.17341/gazimmfd.369347.
Vancouver Aslan M, Akbulut Y, Şengür A, İnce MC. Eklem tabanlı etkili düşme tespiti. GUMMFD. 2017;32(4):1025-34.