Abstract
In the presented system to person fall detection a body-worn accelerometer is used to indicate a potential fall and a ceiling-mounted depth sensor is utilized to authenticate fall alert. In order to expand the observation area the depth sensor has been mounted on a pan-tilt motorized head. If the person acceleration is above a preset threshold the system uses a lying pose detector as well as examines a dynamic feature to authenticate the fall. Thus, more costly fall authentication is not executed frame-by-frame, but instead we fetch from a circular buffer a sequence of depth maps acquired prior to the fall and then process them to confirm fall alert. We show that promising results in terms of sensitivity and specificity can be obtained on publicly available UR Fall Detection dataset.
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References
Friedewald, M., Raabe, O.: Ubiquitous computing: an overview of technology impacts. Telemat. Inf. 28(2), 55–65 (2011)
Wilson, C., Hargreaves, T., Hauxwell, R.: Smart homes and their users: a systematic analysis and key challenges. Pers. Ubiquitous Comp. 19, 463–476 (2015)
Acampora, G., Cook, D., Rashidi, P., Vasilakos, A.: A survey on ambient intelligence in healthcare. Proc. IEEE 101(12), 2470–2494 (2013)
Webster, D., Celik, O.: Systematic review of Kinect applications in elderly care and stroke rehabilitation. J. NeuroEngineering Rehabil. 11, 1 (2014)
Bourke, A., O’Brien, J., Lyons, G.: Evaluation of a threshold-based tri-axial accelerometer fall detection algorithm. Gait Posture 26(2), 194–199 (2007)
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)
Kepski, M., Kwolek, B.: Fall detection on embedded platform using kinect and wireless accelerometer. In: Miesenberger, K., Karshmer, A., Penaz, P., Zagler, W. (eds.) ICCHP 2012, Part II. LNCS, vol. 7383, pp. 407–414. Springer, Heidelberg (2012)
Cuppens, K., Chen, C.W., Wong, K.B.Y., Van de Vel, A., Lagae, L., Ceulemans, B., Tuytelaars, T., Van Huffel, S., Vanrumste, B., Aghajan, H.: Integrating video and accelerometer signals for nocturnal epileptic seizure detection. In: Proceedings of the 14th ACM International Conference on Multimodal Interaction, pp. 161–164, New York, NY (2012)
Chen, C., Jafari, R., Kehtarnavaz, N.: Improving human action recognition using fusion of depth camera and inertial sensors. IEEE Trans. HMS 45, 51–61 (2015)
Spinello, L., Arras, K.: People detection in RGB-D data. In: IEEE/RSJ International Conference on IROS, pp. 3838–3843, September 2011
Chen, J., Kwong, K., Chang, D., Luk, J., Bajcsy, R.: Wearable sensors for reliable fall detection. In: International Conference on Engineering in Medicine and Biology Society, pp. 3551–3554 (2005)
Chang, C.C., Lin, C.J.: LIBSVM: a library for support vector machines. ACM Trans. Intell. Syst. Technol. 2(3), 1–27 (2011)
Kepski, M., Kwolek, B.: Fall detection using ceiling-mounted 3d depth camera. In: International Joint Conference on Computer Vision Theory and Applications (VISAPP), vol. 2, pp. 640–647
Acknowledgment
This work was supported by Polish National Science Center (NCN) under a research grant 2014/15/B/ST6/02808 and a grant 11.11.230.124.
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Kepski, M., Kwolek, B. (2016). Fall Detection Using Body-Worn Accelerometer and Depth Maps Acquired by Active Camera. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_35
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DOI: https://doi.org/10.1007/978-3-319-32034-2_35
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