Abstract
We propose a system to detect accidental fall from walking or sitting activity in a nursing home. Differing from the trajectory tracing techniques, which detects periodic movements, our algorithm explores secondary features (angle and distance), focusing on the correlation between joints and the boundary of this correlation. We generated skeleton joint data using the Kinect sensor because it is affordable and supports sufficiently large capture space. However, other similar smart sensors can also be used. The angle feature denotes the correlation between the normal vector of the floor and the vector formed by linking the knee and ankle (on the left and right leg separately). The distance feature denotes the correlation between the floor and each of several important joints. A fall is reported when the angle is greater than and the distance is less than the respective threshold value. We created an activity database to evaluate our technique. The activities simulate elderly people walking, sitting and falling. Experimental results show that our algorithm is simple to implement, has low computational cost and is able to detect 36/37 falling events, and 57/57 walking and sitting activities accurately.
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References
Kiely, D.K., Kiel, D.P., Burrows, A.B., Lipsitz, L.A.: Identifying nursing home residents at risk for falling. J. Am. Geriatr. Soc. 46, 551–555 (1998)
Winters, J.: Emerging rehabilitative telehealthcare anywhere was the homecare technologies workshop visionary. Emerging and Accessible Telecommunications, Information and Healthcare Technologies, pp. 95–111 (2002)
Viet, V.Q., Lee, G., Choi, D.: Fall detection based on movement and smart phone technology. In: 2012 IEEE RIVF International Conference on Computing and Communication Technologies, Research, Innovation, and Vision for the Future (RIVF), pp. 1–4. IEEE (2012)
Zhuang, X., Huang, J., Potamianos, G., Hasegawa-Johnson, M.: Acoustic fall detection using gaussian mixture models and gmm supervectors. In: IEEE International Conferenceon Acoustics, Speech and Signal Processing (ICASSP), pp. 69–72. IEEE (2009)
Han, J., Shao, L., Xu, D., Shotton, J.: Enhanced computer vision with microsoft kinect sensor: a review. IEEE Trans. Cybern. 43, 1318–1334 (2013)
Bevilacqua, V., Nuzzolese, N., Barone, D., Pantaleo, M., Suma, M., D’Ambruoso, D., Volpe, A., Loconsole, C., Stroppa, F.: Fall detection in indoor environment with kinect sensor. In: 2014 IEEE International Symposium on Innovations in Intelligent Systems and Applications (INISTA) Proceedings, pp. 319–324. IEEE (2014)
Rougier, C., Auvinet, E., Rousseau, J., Mignotte, M., Meunier, J.: Fall detection from depth map video sequences. In: Abdulrazak, B., Giroux, S., Bouchard, B., Pigot, H., Mokhtari, M. (eds.) ICOST 2011. LNCS, vol. 6719, pp. 121–128. Springer, Heidelberg (2011)
Nghiem, A.T., Auvinet, E., Meunier, J.: Head detection using kinect camera and its application to fall detection. In: 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA), pp. 164–169. IEEE (2012)
Stone, E.E., Skubic, M.: Fall detection in homes of older adults using the microsoft kinect. IEEE J. Biomed. Health Inform. 19, 290–301 (2015)
Le, T.L., Morel, J.M., et al.: An analysis on human fall detection using skeleton from microsoft kinect. In: 2014 IEEE 5th International Conference on Communications and Electronics (ICCE), pp. 484–489. IEEE (2014)
Miaou, S.G., Sung, P.H., Huang, C.Y.: A customized human fall detection system using omni-camera images and personal information. In: 1st Transdisciplinary Conference on Distributed Diagnosis and Home Healthcare, D2H2 2006, pp. 39–42. IEEE (2006)
Töreyin, B.U., Dedeoğlu, Y., Çetin, A.E.: HMM based falling person detection using both audio and video. In: Sebe, N., Lew, M., Huang, T.S. (eds.) HCI/ICCV 2005. LNCS, vol. 3766, pp. 211–220. Springer, Heidelberg (2005)
Auvinet, E., Multon, F., Saint-Arnaud, A., Rousseau, J., Meunier, J.: Fall detection with multiple cameras: an occlusion-resistant method based on 3-d silhouette vertical distribution. IEEE Trans. Inf. Technol. Biomed. 15, 290–300 (2011)
Demiris, G., Oliver, D.P., Giger, J., Skubic, M., Rantz, M.: Older adults’ privacy considerations for vision based recognition methods of eldercare applications. Technol. Health Care 17, 41 (2009)
Alazrai, R., Zmily, A., Mowafi, Y.: Fall detection for elderly using anatomical-plane-based representation. In: 2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 5916–5919. IEEE (2014)
Kawatsu, C., Li, J., Chung, C.J.: Development of a fall detection system with microsoft kinect. In: Kim, J.-H., Matson, E., Myung, H., Xu, P. (eds.) Robot Intelligence Technology and Applications. AISC, vol. 208, pp. 623–630. Springer, Heidelberg (2013)
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Flores-Barranco, M.M., Ibarra-Mazano, MA., Cheng, I. (2015). Accidental Fall Detection Based on Skeleton Joint Correlation and Activity Boundary. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2015. Lecture Notes in Computer Science(), vol 9475. Springer, Cham. https://doi.org/10.1007/978-3-319-27863-6_45
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DOI: https://doi.org/10.1007/978-3-319-27863-6_45
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