CFP last date
01 October 2024
Reseach Article

Parkinson’s Fall Detection System using IOT

by Chitimireddi Sushma Sri, Dhrivith Raj, K. Ezhilarasan, K. Karthik Reddy, Chaithrashree Harish
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 185 - Number 16
Year of Publication: 2023
Authors: Chitimireddi Sushma Sri, Dhrivith Raj, K. Ezhilarasan, K. Karthik Reddy, Chaithrashree Harish
10.5120/ijca2023922865

Chitimireddi Sushma Sri, Dhrivith Raj, K. Ezhilarasan, K. Karthik Reddy, Chaithrashree Harish . Parkinson’s Fall Detection System using IOT. International Journal of Computer Applications. 185, 16 ( Jun 2023), 40-43. DOI=10.5120/ijca2023922865

@article{ 10.5120/ijca2023922865,
author = { Chitimireddi Sushma Sri, Dhrivith Raj, K. Ezhilarasan, K. Karthik Reddy, Chaithrashree Harish },
title = { Parkinson’s Fall Detection System using IOT },
journal = { International Journal of Computer Applications },
issue_date = { Jun 2023 },
volume = { 185 },
number = { 16 },
month = { Jun },
year = { 2023 },
issn = { 0975-8887 },
pages = { 40-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume185/number16/32781-2023922865/ },
doi = { 10.5120/ijca2023922865 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:26:15.595211+05:30
%A Chitimireddi Sushma Sri
%A Dhrivith Raj
%A K. Ezhilarasan
%A K. Karthik Reddy
%A Chaithrashree Harish
%T Parkinson’s Fall Detection System using IOT
%J International Journal of Computer Applications
%@ 0975-8887
%V 185
%N 16
%P 40-43
%D 2023
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Parkinson's disease (PD) is one of the long-term regressive disorders of the central nervous system that primarily affects humans' nerve systems, causing frequent falls that can even result in fatalities or put sufferers in dangerous situations. To address this problem, we have put forth a concept that tracks patient falls and alerts the caregiver or family member using a message, call, or alarm to prevent deaths. The wearable fall- detection system for Parkinson's disease sufferers built on a wi-fi module is the main goal of our research and development efforts. The results of an accelerometer, oximeter, and pressure sensor that are uploaded to the Blynk cloud through NodeMCU- ESP32 were used to properly track patient falls.

References
  1. Allen NE, Schwarzel AK, Canning CG. Recurrent falls in Parkinson's disease: a systematic review. Parkinson’s Dis. 2013;2013:906274. doi: 10.1155/2013/906274. Epub 2013 Mar 5. PMID: 23533953; PMCID: PMC3606768.
  2. W. -J. Chang, L. -B. Chen, M. -C. Chen, J. -P. Su, C. -Y. Sie and C. -H. Yang, "Design and Implementation of an Intelligent Assistive System for Visually Impaired People for Aerial Obstacle Avoidance and Fall Detection," in IEEE Sensors Journal, vol. 20, no. 17, pp. 10199-10210, 1 Sept.1, 2020, doi: 10.1109/JSEN.2020.2990609.
  3. Wu F, Zhao H, Zhao Y, Zhong H. Development of a wearable-sensor-based fall detection system. Int J Telemed Appl. 2015;2015:576364. doi: 10.1155/2015/576364. Epub 2015 Feb 16. PMID: 25784933; PMCID: PMC4346101.
  4. B. Aguiar, T. Rocha, J. Silva and I. Sousa, "Accelerometer- based fall detection for smartphones," 2014 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 2014, pp. 1-6, doi: 10.1109/MeMeA.2014.6860110
  5. Luo, X., Liu, T., Liu, J. et al. Design and implementation of a distributed fall detection system based on wireless sensor networks. J Wireless Com Network 2012, 118 (2012). https://doi.org/10.1186/1687-1499-2012-118.J. S. Navya, S. Rajasree and B. Ullas, "Non-Invasive Fall Detection System for Parkinson's Disease," 2018 International CET Conference on Control, Communication, and Computing (IC4), 2018, pp. 157-160, doi: 10.1109/CETIC4.2018.8530892.
  6. P. Cao and C. -H. Min, "Fall Prediction in People with Parkinson's Disease," 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022, pp. 1502-1505, doi: 10.1109/EMBC48229.2022.9872013.
  7. Priya Sharma, Parveen Kantha, "Blynk cloud server-based monitoring and control using NodeMCU," 2020 International Research Journal of Engineering and Technology (IRJET), vol 7.
  8. B.Q. Tran, Q.T. Huynh, U.D. Nguyen, L.B. Irazabal, N. Ghassemian, Optimaization of an accelerometer and gyroscope-based fall detection algoritgm, J. Sensors 2015 (2051) 8.
  9. M. F. R. Al-Okby and S. S. Al-Barrak, "New Approach for Fall Detection System Using Embedded Technology," 2020 IEEE 24th International Conference on Intelligent.
  10. Engineering Systems (INES), 2020, pp. 209-214, doi: 10.1109/INES49302.2020.914717
Index Terms

Computer Science
Information Sciences

Keywords

Parkinson’s disease fall-detection Blynk cloud.