Real time fall detection in fog computing scenario (original) (raw)
References
Rubenstein, L.Z.: Falls in older people: epidemiology, risk factors and strategies for prevention. Age Ageing 35, ii37–ii41 (2006) Article Google Scholar
Bonomi, F., Milito, R., Zhu, J., Addepalli, S.: Fog computing and its role in the internet of things. In Proceedings of the First Edition of the MCC Workshop on Mobile Cloud Computing, pp. 13–16. ACM (2012)
Gope, P., Hwang, T.: BSN-Care: a secure IoT-based modern healthcare system using body sensor network. IEEE Sens. J. 16, 1368–1376 (2015) Article Google Scholar
Tyagi, S., Agarwal, A., Maheshwari, P.: A conceptual framework for IoT-based healthcare system using cloud computing, 6th International Conference-Cloud System and Big Data Engineering (Confluence), pp. 503–507, IEEE, India (2016)
Greene, S., Thapliyal, H., Carpenter, D.: IoT-based fall detection for smart home environments. In 2016 IEEE international symposium on nanoelectronic and information systems (iNIS), pp. 23–28, IEEE, India (2016)
Gia, T.N., Tcarenko, I., Sarker Victor, K., Rahmani, A.M., Westerlund, T., Liljeberg, P., Tenhunen, H.: Iot-based fall detection system with energy efficient sensor nodes. In 2016 IEEE Nordic Circuits and Systems Conference (NORCAS), pp. 1–6, IEEE, India (2016)
Tcarenko, I., Gia, T.N., Tcarenko, I., Sarker, V.K., Rahmani, A.M., Westerlund, T., Liljeberg, P., Tenhunen, H.: Energy-efficient iot-enabled fall detection system with messenger-based notification. In International Conference on Wireless Mobile Communication and Healthcare, pp. 19–26, Springer, India (2016)
Hsieh, Y.-Z., Jeng, Y.-L.: Development of home intelligent fall detection IoT system based on feedback optical flow convolutional neural network. IEEE Access 6, 6048–6057 (2017) Article Google Scholar
Ngu, A., Yeahuay, W., Zare, H., Polican, A., Yarbrough, B., Yao, L.: Fall detection using smartwatch sensor data with accessor architecture. International Conference on Smart Health, pp. 81–93. Springer, India (2017)
Hsu, C.C.-H., Wang, M.Y.-C., Shen, H.C.H., Chiang, R.H-C., Wen, C.H.P.: FallCare+: an IoT surveillance system for fall detection. In 2017 International Conference on Applied System Innovation (ICASI), pp. 921–922, IEEE, India (2017)
Mauldin, T., Canby, M., Metsis, V., Ngu, A., Rivera, C.: SmartFall: a smartwatch-based fall detection system using deep learning. Sensors 18, 3363 (2017) Article Google Scholar
Yacchirema, D., Suárez, J., de Puga, C., Palau, M.E.: Fall detection system for elderly people using IoT and big data. Proced. Comput. Sci. 130, 603–613 (2018) Article Google Scholar
Chandra, I., Sivakumar, N., Gokulnath, C.B., Parthasarathy, P.: IoT based fall detection and ambient assisted system for the elderly. Clust. Comput. 22, 2517–2525 (2019) Article Google Scholar
Medrano, C., Igual, R., Plaza, I., Castro, M.: Detecting falls as novelties in acceleration patterns acquired with smartphones. PLoS ONE 9, e94811 (2014) Article Google Scholar
Klenk, J., Schwickert, L., Palmerini, L., Mellone, S., Bourke, A., Ihlen, E.A.F., Kerse, N., Hauer, K., Pijnappels, M., Synofzik, M., et al.: The FARSEEING real-world fall repository: a large-scale collaborative database to collect and share sensor signals from real-world falls. Eur. Rev. Aging Phys. Act. 13, 8 (2016) Article Google Scholar
Schölkopf, B., Smola, A.J., Williamson, R.C., Bartlett, P.L.: New support vector algorithms. Neural Comput. 12, 1207–1245 (2000) Article Google Scholar
Li, P., Samorodnitsk, G., Hopcroft, J.: Sign cauchy projections and chi-square kernel. In Advances in Neural Information Processing Systems, pp. 2571–2579 (2013)