Abstract
In general there is a steep increase in the number of cases related to elderly people falling down and getting hospitalized since they are living alone. This increases the need for an efficient and low cost surveillance based fall detection system. Wireless video sensor network (WVSN) can be used for such surveillance applications like monitoring elderly people at home, old age homes or hospitals. But there are some limitations in WVSN like memory constraint, low bandwidth and limited battery life. A light weight fall detection algorithm with efficient encoding technique is needed to make WVSN suitable for health care applications. In this paper a simple feature based fall detection system using compressed sensing algorithm is proposed and it is compared with the existing method. This proposed framework shows 82.5% reduction in time and 83.75% reduction in energy compared to raw frame transmission. The average percentage of space saving achieved by this proposed work is 83.81% which shows 30% increase when compared to the existing method.
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Rashid, B., & Rehmani, M. H. (2016). Applications of wireless sensor networks for urban areas: A survey. Journal of Network and Computer Applications, 60, 192–219.
Lindemann, U., Hock, A., Stuber, M., Keck, W., & Becker, C. (2005). Evaluation of a fall detector based on accelerometers: A pilot study. Medical and Biological Engineering and Computing, 43(5), 548–551.
Zhang, T., Wang, J., Xu, L., & Liu, P. (2006), Using wearable sensor and NMF algorithm to realize ambulatory fall detection. In International conference on natural computation, Springer, Berlin.
Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–114.
Selvabala, V. S. N., & Ganesh, A. B. (2012). Implementation of wireless sensor network based human fall detection system. Procedia Engineering, 30, 767–773.
Akyildiz, I. F., Melodia, T., & Chowdhury, K. R. (2007). A survey on wireless multimedia sensor networks. Computer Networks, 51(4), 921–960.
Angayarkanni, V., & Radha, S. (2016). Design of bandwidth efficient compressed sensing based prediction measurement encoder for video transmission in wireless sensor networks. Wireless Personal Communications, 88(3), 553–557.
Madhubala, S., & Umamakeswari, A. (2015). A survey on technical approaches in fall detection system. National Journal of Physiology, Pharmacy and Pharmacology, 5(4), 275–279.
Zhang, Z., Conly, C., & Athitsos, V. (2015). A survey on vision-based fall detection. In Proceedings of the 8th ACM international conference on PErvasive technologies related to assistive environments. ACM.
Yang, L., Ren, Y., Hu, H., & Tian, B. (2015). New fast fall detection method based on spatio-temporal context tracking of head by using depth images. Sensors, 15(9), 23004–23019.
Williams, A., Ganesan, D., & Hanson, A. (2007). Aging in place: Fall detection and localization in a distributed smart camera network. In Proceedings of the 15th ACM international conference on Multimedia. ACM.
Madhubala, J. S., & Umamakeswari, A. (2015). A vision based fall detection system for elderly people. Indian Journal of Science and Technology, 8(S9), 172–180.
Díaz-Ramírez, A., Domínguez, E., & Martínez-Alvarado, L. (2015). A falls detection system for the elderly based on a WSN. In International symposium on technology and society (ISTAS), IEEE
Neggazi, M., Hamami, L., & Amira, A. (2014). Efficient compressive sensing on the shimmer platform for fall detection. In IEEE international symposium on circuits and systems (ISCAS), IEEE.
http://www.instructables.com/id/PIR-Motion-Sensor-Tutorial/.
Luo, X., et al. (2012). Design and implementation of a distributed fall detection system based on wireless sensor networks. EURASIP Journal on Wireless Communications and Networking, 1, 118.
Donoho, D. L. (2006). Compressed sensing. IEEE Transactions on Information Theory, 52(4), 1289–1306.
Candes, E. J. (2008). The restricted isometry property and its implications for compressed sensing. Comptes Rendus Mathematique, 346(9–10), 589–592.
Yin, M., Yu, K., & Wang, Z. (2016). Compressive sensing based sampling and reconstruction for wireless sensor array network. Mathematical Problems in Engineering.
Aruna, N., Angayarkanni, V., & Radha, S. (2015). Compressed sensing based quantization with prediction encoding for video transmission in WSN. In IEEE International conference on computation of power, energy information and commuincation (ICCPEIC).
Cai, T. T., & Wang, L. (2011). Orthogonal matching pursuit for sparse signal recovery with noise. IEEE Transactions on Information Theory, 57(7), 4680–4688.
Charfi, I., Mitéran, J., Dubois, J., Atri, M., & Tourki R. (2012). Definition and performance evaluation of a robust SVM based fall detection solution. In 8th International conference on signal image technology and internet based systems (SITIS).
Auvinet, E., Rougier, C., Meunier, J., St-Arnaud, A., & Rousseau, J. (2010) Multiple cameras fall dataset. Technical report 1350, DIRO - Université de Montréal, July.
Yunus, F., Sharifah, H. S., Ariffin, S. K., & Ismail, N. S. (2013). Optimum parameters for MPEG-4 data over wireless sensor network. International Journal of Engineering & Technology, 5(5), 0975-4024.
Amjad, M., Sharif, M., Afzal, M. K., & Kim, S. W. (2016). TinyOS-new trends, comparative views, and supported sensing applications: A review. IEEE Sensors Journal, 16(9), 2865–2889.
Akhtar, F., & Rehmani, M. H. (2015). Energy replenishment using renewable and traditional energy resources for sustainable wireless sensor networks: A review. Renewable and Sustainable Energy Reviews, 45, 769–784.
Nandhini, S. A., Sankararajan, R., & Rajendiran, K. (2015). Video compressed sensing framework for wireless multimedia sensor networks using a combination of multiple matrices. Computers & Electrical Engineering, 44, 51–66.
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Veeraputhiran, A., Sankararajan, R. Feature based fall detection system for elders using compressed sensing in WVSN. Wireless Netw 25, 287–301 (2019). https://doi.org/10.1007/s11276-017-1557-3
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DOI: https://doi.org/10.1007/s11276-017-1557-3