Implementasi Sistem Notifikasi untuk Pengawasan Pasien Alzheimer Berbasis Bluetooth Low Energy (BLE)
Abstract
Alzheimer's patients need attention and special treatment due to their inability to remember something. One technology that is widely used for tracking objects or people in an indoor environment is a Bluetooth Low Energy (BLE). In this paper, a surveillance notification system for Alzheimer's patients is proposed using Beacon technology to prevent the lossof patients. Improvement in accuracy of the estimated position of the patient were calculated using a Kalman filter. The reason for using this method was the difficulty of determining the location of objects due to noise and inaccuracy of measurement data.Fromthe results of the tests performed, it can be seen that the system made is able to provide notifications to nurses if the patient exceeds the specified distance with an average success of up to 90%. The use of the Kalman method is also able to increase the accuracy of the estimation of patient position with an estimated error reduction of 69.7%.
References
P. Thakare dan V.R. Pawar, “Alzheimer Disease Detection and Tracking of Alzheimer Patient,” Proc. ICICT’16, 2016, hal. 1-4.
A. Pratiarso, A.I. Imanuddin, M. Yuliana, P. Kristalina, dan I.G.P Astawa, “Implementation of Kalman Filter Method for Tracking Position of Alzheimer's Patients,” Proc. ICON-SONICS’17, 2017, hal 135-140.
M.B. Mendoza, C.A. Bergado, J.L.B.D. Castro, dan R.G.T. Siasat, “Tracking System for Patients with Alzheimer's Disease in a Nursing Home,” Proc. TENCON’17, 2017, hal. 2566-2570.
F.S. Danis dan A.T. Cemgil, “Model-Based Localization and Tracking Using Bluetooth Low-Energy Beacons,” J. Sensors, Vol. 17, hal. 1-23, 2017.
D. Chen, K.G. Shin, Y. Jiang, dan K. Kim, “Locating and Tracking BLE Beacons with Smartphones,” Proc. CoNEXT’17, 2017, hal. 263-275.
A.H. Oleval, “Indoor Navigation and Personal Tracking System Using Bluetooth Low Energy Beacons”, Thesis, Uppsala Universitet, Uppsala, Sweden, Okt. 2017.
P. Kristalina, Wirawan, dan G. Hendrantoro, “DOLLY: An Experimental Evaluation of Distributed Node Positioning Framework in Wireless Sensor Networks”, Proc. ISSNIP’14, 2014, hal. 1-6.
N.H. Ali dan G.M. Hassan, “Kalman Filter Tracking,” Int.J. of Computer Applications, Vol. 89, No. 9, hal. 15-18, 2014.
A. Pratiarso, A.S. Putra, P. Kristalina, A. Sudarsono, M. Yuliana, dan I.G.P. Astawa, “Skema Lokalisasi Posisi Node Terdistribusi pada Lingkungan Free Space Path Loss, ” Jurnal Nasional Teknik Elektro dan Teknologi Informasi (JNTETI), Vol. 6, No. 3, hal. 352-358, 2017.
H.A. Patel dan D.G. Thakore, “Moving Object Tracking using Kalman Filter,” International Journal of Computer Science and Mobile Computing (IJCSMC), Vol. 2, No. 4, hal. 326 – 332, April 2013.
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