ABSTRACT
This paper focused on the development of a system that could detect and locate falls, and identify the victims in a short period of time. The system used triaxial-accelerometer in detecting a fall, signal strengths from access points in locating the position, and media access control (MAC) addresses in identifying the name of the victim. Wemos D1 was used as the microcontroller in measuring and averaging signal strengths, computing the resultant acceleration coming from the MPU6050 triaxial-accelerometer and sending the values together with the MAC address to the database. The software developed accesses the database, computes for the location, and displays the outputs to the user while sounding an alarm. To test its functionality, different categories of testing were conducted. The fall function was tested and produced a recall of 100% and a precision of 97.5%. The response time was measured by how much time it took from the event of the fall to the software displaying the location and sounding the alarm. The computed average response time was 1.1128 seconds and was considered low and fast enough. The displaying of the location was tested while considering the size of the area of the testing. The area considered had the size of 10 m by 6 m and the test produced accuracies of 82.8% and 90% on x and y axes respectively. This means that the margin of error for the x-axis was 1.72 m and 0.6 m on the y-axis. In the end, the fall detection system was able to perform its function and provide reliable output that could help elderly institution, as well as elderly people, to lessen the risks and consequences of a fall.
- D. o. S. W. a. D. a. t. D. o. Health, "Community Services for the Elderly in the Philippines:A Collaboration of the Department of Social Welfare and Development and the Department of Health," 5TH ASEAN and Japan High Level Officials Meeting on Caring Societies, 2007.Google Scholar
- N. S. Office, "A Special Report on Senior Citizen," 2000.Google Scholar
- D. o. Health, "LEADING CAUSES OF MORTALITY," 2010.Google Scholar
- N. C. o. A. (NCOA), "Fall Statistical Facts."Google Scholar
- G. o. W. A. D. o. Health, "Consequences of Falls."Google Scholar
- H. i. Aging, "Physical Risk Factors," 2017.Google Scholar
- R. Igual, C. Medrano, and I. Plaza, "Challenges, issues and trends in fall detection systems," Biomedical engineering online, vol. 12, p. 66, 2013.Google ScholarCross Ref
- N. Noury, A. Fleury, P. Rumeau, A. Bourke, G. Laighin, V. Rialle, , "Fall detection-principles and methods," in 2007 29th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2007, pp. 1663-1666.Google Scholar
- Q. Li, J. A. Stankovic, M. A. Hanson, A. T. Barth, J. Lach, and G. Zhou, "Accurate, Fast Fall Detection Using Gyroscopes and Accelerometer-Derived Posture Information," in BSN, 2009, pp. 138-143.Google Scholar
- M. A. Guvensan, A. O. Kansiz, N. C. Camgoz, H. Turkmen, A. G. Yavuz, and M. E. Karsligil, "An Energy-Efficient Multi-Tier Architecture for Fall Detection on Smartphones," Sensors, vol. 17, p. 1487, 2017.Google ScholarCross Ref
- A. Hakim, M. S. Huq, S. Shanta, and B. Ibrahim, "Smartphone based data mining for fall detection: Analysis and design," Procedia computer science, vol. 105, pp. 46-51, 2017.Google ScholarDigital Library
- F. College, "The RAD life cycle."Google Scholar
- T.-M. Dinh and N.-S. Duong, "Smartphone Indoor Positioning System based on BLE iBeacon and Reliable region-based position correction algorithm," in 2019 International Conference on Advanced Technologies for Communications (ATC), 2019, pp. 264-268.Google Scholar
- T.-N. Lin and P.-C. Lin, "Performance comparison of indoor positioning techniques based on location fingerprinting in wireless networks," in 2005 international conference on wireless networks, communications and mobile computing, 2005, pp. 1569-1574.Google Scholar
- I. Inc., "MPU-6000 and MPU-6050 Register Map and Descriptions Revision 4.2," 2013.Google Scholar
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