Main Article Content
Many falls usually lead to chronic complications for the elderly. Four common causes of falls include slippery floors, the level of brightness, stairs, and residential obstacles such as a rug. These are linked to household activities, and therefore, the fatal falls often occur when an elderly person stays home alone, a timely contact to the doctor is not possible.
Objective: The study aimed to apply a dynamic functionality embedded in a microcontroller to detect true falls and activate alarms, promptly.
Methods: The accelerometer embedded in Arduino NANO 33 IoT measured the 3-axis acceleration in the gait cycle. The 3-axis acceleration characterized the dominant frequency and mean peak. These two characteristics could distinguish between real falls and fake falls. Actual falls were defined as the ability to continue moving after a fall. Acceleration data was then analyzed using the double integration to find the foot clearance in the four most common causes of falls.
Results: The study demonstrated that foot clearance was decreased in the four situations and that Arduino NANO 33 IoT could accurately distinguish between real falls and fake falls, proving the device's ability to detect falls that constitute an emergency.
Conclusions: The potential for the Arduino Nano 33 IoT was confirmed to detect falls in senior citizens through this study. The Arduino Nano could distinguish between real falling and fake falling, regardless of whether it is walking or running before the fall.
Sachiyo Yoshida. A global report on falls prevention: epidemiology of falls. Ageing and Life Course, Family and Community Health, World Health Organization.
Tony Rosen, Karin A. Mack, Rita K. Noonan. Slipping and tripping: Fall injuries in adults associated with rugs and carpets. J. Inj. Violence Research. 2013;5(1):61-69.
NIH, “Prevent Falls and Fractures”, National Institute on Aging.
Pankaj Mehta, Ching-Hao Wang, Alexandre G. R. Day, Clint Richardson, Marin Bukov, Charles K. Fisher, David J. Schwab. A high-bias, low-variance introduction to Machine Learning for physicists; 2019.
Yaqing Wang, Quanming Yao, James T. Kwok, Lionel M. Ni. Generalizing from a few examples: A survey on few-shot learning. ACM Comput. Surv. 2020;1(1):1-34.
Conor McDonald. Machine learning fundamentals (II): Neural networks. M, Towards Data Science.
Neeraj R. Rathi. A design of low power wearable system for pre-fall detection. Master of Science in Electrical and Computer Engineering, Purdue University, Indianapolis, Indiana; 2018.
Leroy L. Long, III and Manoj Srinivasan. Walking, running, and resting under time, distance, and average speed constraints: optimality of walk-run-rest mixtures. J. R. Soc. Interface. 2013;10(81):1-10.
Kristin D. Morgan, Brian Noehren, Arezoo Eshraghi. Identification of knee gait waveform pattern alterations in individuals with patellofemoral pain using fast Fourier transform. PLoS One. 2018;13(12):1- 11.
MathWorks. Practical introduction to frequency-domain analysis. R 2020.
Alvaro Uro-de-la-Herran, Begonya Garcia-Zapirain, Amaia Mendex-Zorilla. Gait analysis methods: An overview of wearable and non-wearable systems, highlighting clinical applications. Sensors (Basel). 2014;14(2): 3362-3394.
Joseph N. Pato, Lynette I. Millett. Biometric recognition: Challenges and opportunities. Washington, National Academic Press (US); 2010.
Mourad Benoussaad, Benoit Sijobert, Katja Mombaur, Christine Azevedo Coste. Robust foot clearance estimation based on the Integration of Foot-Mounted IMU Acceleration Data. Sensors. 2016;16:12.
Lisa Alcock, Brook Galna, Ruth Perkins, Sue Lord, Lynn Rochester. Step length determines minimum toe clearance in older adults and people with Parkinson’s disease. J. Biomech. 2018;71:30-36.
Yumi Ono, Koyu, Hiroki Ora, Yuki Hirobe, Yufeng Mao, Hiroyuki Sawada, Akira Inaba, Satoshi Orimo, Yoshihiro Miyake. Inertial measurement unit-based estimation of foot trajectory for clinical gait analysis. 2019; 1-22. Available:https://www.biorxiv.org/content/10.1101/595496v1.full.pdf