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Eklem tabanlı etkili düşme tespiti

Year 2017, Volume: 32 Issue: 4, 1025 - 1034, 08.12.2017
https://doi.org/10.17341/gazimmfd.369347

Abstract

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.

References

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  • 2. Nüfus Projeksiyonları 2013-2075. http://www.tuik.gov.tr/PreHaberBultenleri.do?id=15844. Yayın tarihi Şubat 14, 2013. Erişim tarihi Ocak 10, 2015.
  • 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.
  • 19. Dubois A., Charpillet F., Automatic Fall Detection System with a RGB-D Camera using a Hidden Markov Model, 11th International Conference on Smart Homes and Health Telematics (ICOST 2013), Singapore-Singapore, 259-266, 19-21 June, 2013.
  • 20. Zhang C., Tian Y., Rgb-d camera-based daily living activity recognition. Journal of Computer Vision and Image Processing, 2 (4), 1-7, 2012.
  • 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.
  • 24. Chen X., Koskela M., Skeleton-based action recognition with extreme learning machines, Neurocomputing, 149, 387-396, 2015.
  • 25. Chaaraoui A.A., Flórez-Revuelta F., Optimizing human action recognition based on a cooperative coevolutionary algorithm. Engineering Applications of Artificial Intelligence, 31, 116-125, 2014.
  • 26. Shotton J., Sharp T., Kipman A., Fitzgibbon A., Finocchio M., Blake A., Moore R., Real-time human pose recognition in parts from single depth images. Communications of the ACM, 56 (1), 116-124, 2013.
  • 27. Hussein M.E., Torki M., Gowayyed M.A., El-Saban M., Human Action Recognition Using a Temporal Hierarchy of Covariance Descriptors on 3D Joint Locations, Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, Beijing-China, 2466-2472, 3-9 Agust, 2013.
  • 28. Fukunaga K., Parametric Classifiers, Introduction to Statistical Patern Recognition, Academic Press Limited,USA, 125-180, 1990.
  • 29. Alcin O.F., Sengur A., Ince M. C., Forward-backward pursuit based sparse extreme learning machine, Journal of The Faculty of Engineering and Architecture of Gazi University, 30 (1), 111-117, 2015.
  • 30. Burges C.J., A tutorial on support vector machines for pattern recognition, Data mining and knowledge discovery, 2 (2), 121-167, 1998.
  • 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.
  • 32. Zhu Y., Chen W., Guo G., Fusing spatiotemporal features and joints for 3d action recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland Oregon-USA, 486-491, 23-28 June, 2013.
  • 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.
Year 2017, Volume: 32 Issue: 4, 1025 - 1034, 08.12.2017
https://doi.org/10.17341/gazimmfd.369347

Abstract

References

  • 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.
  • 2. Nüfus Projeksiyonları 2013-2075. http://www.tuik.gov.tr/PreHaberBultenleri.do?id=15844. Yayın tarihi Şubat 14, 2013. Erişim tarihi Ocak 10, 2015.
  • 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.
  • 19. Dubois A., Charpillet F., Automatic Fall Detection System with a RGB-D Camera using a Hidden Markov Model, 11th International Conference on Smart Homes and Health Telematics (ICOST 2013), Singapore-Singapore, 259-266, 19-21 June, 2013.
  • 20. Zhang C., Tian Y., Rgb-d camera-based daily living activity recognition. Journal of Computer Vision and Image Processing, 2 (4), 1-7, 2012.
  • 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.
  • 24. Chen X., Koskela M., Skeleton-based action recognition with extreme learning machines, Neurocomputing, 149, 387-396, 2015.
  • 25. Chaaraoui A.A., Flórez-Revuelta F., Optimizing human action recognition based on a cooperative coevolutionary algorithm. Engineering Applications of Artificial Intelligence, 31, 116-125, 2014.
  • 26. Shotton J., Sharp T., Kipman A., Fitzgibbon A., Finocchio M., Blake A., Moore R., Real-time human pose recognition in parts from single depth images. Communications of the ACM, 56 (1), 116-124, 2013.
  • 27. Hussein M.E., Torki M., Gowayyed M.A., El-Saban M., Human Action Recognition Using a Temporal Hierarchy of Covariance Descriptors on 3D Joint Locations, Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence, Beijing-China, 2466-2472, 3-9 Agust, 2013.
  • 28. Fukunaga K., Parametric Classifiers, Introduction to Statistical Patern Recognition, Academic Press Limited,USA, 125-180, 1990.
  • 29. Alcin O.F., Sengur A., Ince M. C., Forward-backward pursuit based sparse extreme learning machine, Journal of The Faculty of Engineering and Architecture of Gazi University, 30 (1), 111-117, 2015.
  • 30. Burges C.J., A tutorial on support vector machines for pattern recognition, Data mining and knowledge discovery, 2 (2), 121-167, 1998.
  • 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.
  • 32. Zhu Y., Chen W., Guo G., Fusing spatiotemporal features and joints for 3d action recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Portland Oregon-USA, 486-491, 23-28 June, 2013.
  • 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.
There are 32 citations in total.

Details

Journal Section Makaleler
Authors

Muzaffer Aslan 0000-0002-2418-9472

Yaman Akbulut

Abdulkadir Şengür

Melih Cevdet İnce This is me

Publication Date December 8, 2017
Submission Date March 8, 2016
Acceptance Date July 2, 2017
Published in Issue Year 2017 Volume: 32 Issue: 4

Cite

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. December 2017;32(4):1025-1034. doi:10.17341/gazimmfd.369347
Chicago Aslan, Muzaffer, Yaman Akbulut, Abdulkadir Şengür, and Melih Cevdet İnce. “Eklem Tabanlı Etkili düşme Tespiti”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 32, no. 4 (December 2017): 1025-34. https://doi.org/10.17341/gazimmfd.369347.
EndNote Aslan M, Akbulut Y, Şengür A, İnce MC (December 1, 2017) Eklem tabanlı etkili düşme tespiti. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 32 4 1025–1034.
IEEE M. Aslan, Y. Akbulut, A. Şengür, and M. C. İnce, “Eklem tabanlı etkili düşme tespiti”, GUMMFD, vol. 32, no. 4, pp. 1025–1034, 2017, doi: 10.17341/gazimmfd.369347.
ISNAD Aslan, Muzaffer et al. “Eklem Tabanlı Etkili düşme Tespiti”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi 32/4 (December 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 et al. “Eklem Tabanlı Etkili düşme Tespiti”. Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, vol. 32, no. 4, 2017, pp. 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.