Login Register Cart 0
Generic placeholder image

Recent Advances in Computer Science and Communications

Editor-in-Chief

ISSN (Print): 2666-2558
ISSN (Online): 2666-2566

Back
Journal Home About Journal Editorial Board Current Issue Volumes/Issues
Subscribe
Research Article

VitaFALL: Advanced Multi-Threshold Based Reliable Fall Detection System

Author(s): Warish D. Patel*, Chirag Patel and Monal Patel

Volume 15, Issue 1, 2022

Published on: 04 September, 2020

Page: [32 - 39] Pages: 8

DOI: 10.2174/2666255813999200904132939

Price: $65

Abstract

Background: The biggest challenge in our technologically advanced society is the healthy being of aging individuals and differently-abled people in our society. The leading cause for significant injuries and early death in senior citizens and differently-abled people is due to falling off. The possibility to automatically detect falls has increased demand for such devices, and the high detection rate is achieved using the wearable sensors, this technology has a quite social and monetary impact on society. Therefore, even for the daily activity in the life of aged people, an automatic fall detecting system and vital signs examining system have become a necessity.

Objectives: This research work aims at helping aged people and every other necessary human by monitoring their vital signs and fall prediction. A fall detection VitaFALL (Vital Signs and Fall Monitoring) device could analyze the measurement in all three orthogonal directions using a tripleaxis accelerometer, and Vital Signs Parameters (Heartrate, Heartbeat, and Temperature monitoring) for the aged and differently-abled people.

Methods: In comparison with present algorithms, there are various benefits regarding privacy, success rate, and design of devices upgraded using an implemented algorithm over the ubiquitous algorithm.

Results: As concluded from the experimental outcomes, the accuracy achieved is up to 94%. ADXL335 is a 3-Axial Accelerometer Module that collects the accelerations of aged people from a VitaFALL device. A guardian can be notified by sending a text message via GSM and GPRS modules so that the aged people can be helped.

Conclusion: However, a delay in the time can be noticed while comparing the gradient and minimum value to predetermine the state of the older person. The results of the experiment show the adequacy of the proposed approach.

Keywords: VitaFALL, i-NXTGeUH, triaxial accelerometer, Activity of Daily Living (ADL), fall prediction and detection, Internet of Medical Things (IoMT), vital signs, elderly and differently-abled people, multi-threshold, wellness.

Graphical Abstract

[1]
A. Shahzad, and K. Kim, "FallDroid: An automated smart-phone-based fall detection system using multiple kernel learning", IEEE Trans. Industr. Inform., vol. 15, no. 1, pp. 35-44, 2019.
[http://dx.doi.org/10.1109/TII.2018.2839749]
[2]
W.D. Patel, C.I. Patel, and C. Valderrama, "IoMT based efficient vital signs monitoring system for elderly healthcare using neural network", Int. J. Res., pp. 239-245. 2018.2236-6124 16.10089.IJR.2018.V8I1.285311.234454
[3]
W. D. Patel, and C. I. Patel, "Smart health: Natural language pro-cessing based question and answering retrieval system in healthca-re", Int. J. Emerg. Technol. Innov. Res., vol. 6, no. 5, pp. 127-137.
[4]
W.D. Patel, S. Pandya, B. Koyuncu, B. Ramani, S. Bhaskar, and H. Ghayvat, "I-NXTGeUH: LoRaWAN based NEXT generation ubi-quitous healthcare system for vital signs monitoring & falls detec-tion", In: 2018 IEEE Punecon, 2018, pp. 1-8.
[http://dx.doi.org/10.1109/PUNECON.2018.8745431]
[5]
W. Patel, S. Pandya, and V. Mistry, "i-MsRTRM: Developing an IoT based intelligent medicare system for real-time remote health monitoring", In: In 2016 8th International Conference on Computational Intelligence and Communication Networks, 2016, pp. 641-645.
[http://dx.doi.org/10.1109/CICN.2016.132]
[6]
D.A. Konan, and W. Patel, "i-NXGeVita: IoMT based ubiquitous health monitoring system using deep neural networks", In 2018 3rd IEEE International Conference on Recent Trends in Electronics, Information and Communication Technology,, 2018pp. 552-557
[7]
M.G. Amin, Y.D. Zhang, F. Ahmad, and K.D. Ho, "Radar signal processing for elderly fall detection: The future for in-home moni-toring", IEEE Signal Process. Mag., vol. 33, no. 2, pp. 71-80, 2016.
[http://dx.doi.org/10.1109/MSP.2015.2502784]
[8]
Y. Wang, K. Wu, and L.M. Ni, "WiFall: Device-free fall detection by wireless networks", IEEE Trans. Mobile Comput., vol. 16, pp. 581-594, 2018.
[http://dx.doi.org/10.1109/TMC.2016.2557792]
[9]
T.P. Haines, and A.M. Hill, "Better off doing falls prevention “With” our patients rather than “To” them?", Jt. Comm. J. Qual. Patient Saf., vol. 46, no. 3, pp. 127-128, 2020.
[http://dx.doi.org/10.1016/j.jcjq.2020.01.004] [PMID: 32111349]
[10]
V.S. Kumar, K.G. Acharya, B. Sandeep, T. Jayavignesh, and A. Chaturvedi, "“Wearable sensor-based human fall detection wireless system”, Wireless Communication Networks and Internet of Things. Lecture Notes In Electrical Engineering, Springer", Singa-pore, vol. 493, pp. 217-234, 2019.
[http://dx.doi.org/10.1007/978-981-10-8663-2_23]
[11]
Y. Lee, H. Yeh, K.H. Kim, and O. Choi, "A real-time fall detection system based on the acceleration sensor of a smartphone", Int. J. Eng. Bus. Manag., vol. 10, no. 2, pp. 1-8, 2018.
[http://dx.doi.org/10.1177/1847979017750669]
[12]
Y. Nizam, M.N. Mohd, and M. Jamil, "Development of a user-adaptable human fall detection based on fall risk levels using depth sensor", Sensors, vol. 18, no. 7, p. 2260, 2018.
[http://dx.doi.org/10.3390/s18072260] [PMID: 30011823]
[13]
X. Xi, W. Jiang, Z. Lü, S.M. Miran, and Z.Z. Luo, "Daily activity monitoring and fall detection based on surface electromyography and plantar pressure", Complexity, vol. 2020, pp. 1-12, 2020.
[http://dx.doi.org/10.1155/2020/9532067]
[14]
P. Pierleoni, L. Pernini, A. Belli, L. Palma, S. Valenti, and M. Pa-niccia, "SVM-based fall detection method for elderly people using Android low-cost smartphones", In: IEEE Sensors Applications Sym-posium, 2015, pp. 1-5.
[15]
J.A. Sanchez, and D.M. Muñoz, "Fall detection using accelerome-ter on the user’s wrist and artificial neural networks", In XXVI Bra-zilian Congress on Biomedical Engineering, 2019pp. 641-647
[http://dx.doi.org/10.1007/978-981-13-2119-1_98]
[16]
J.K. Lee, S.N. Robinovitch, and E.J. Park, "Inertial sensing-based pre-impact detection of falls involving near-fall scenarios", IEEE Trans. Neural Syst. Rehabil. Eng., vol. 23, no. 2, pp. 258-266, 2015.
[http://dx.doi.org/10.1109/TNSRE.2014.2357806] [PMID: 25252283]
[17]
B. Kwolek, and M. Kepski, "Improving fall detection by the use of depth sensor and accelerometer", Neurocomputing, vol. 168, pp. 637-645, 2016.
[http://dx.doi.org/10.1016/j.neucom.2015.05.061]
[18]
J. Liu, and T.E. Lockhart, "Development and evaluation of a prior-to-impact fall event detection algorithm", IEEE Trans. Biomed. Eng., vol. 61, no. 7, pp. 2135-2140, 2014.
[http://dx.doi.org/10.1109/TBME.2014.2315784] [PMID: 24718566]
[19]
X. Ma, H. Wang, B. Xue, M. Zhou, B. Ji, and Y. Li, "Depth-based human fall detection via shape features and improved extreme lear-ning machine", IEEE J. Biomed. Health Inform., vol. 18, no. 6, pp. 1915-1922, 2014.
[http://dx.doi.org/10.1109/JBHI.2014.2304357] [PMID: 25375688]

Rights & Permissions Print Cite
Article Metrics
11
1
© 2024 Bentham Science Publishers | Privacy Policy