Research Article
Parkinson’s Fall Detection System using IOT
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
|
| Volume 185 - Issue 16 |
| Published: Jun 2023 |
| Authors: Chitimireddi Sushma Sri, Dhrivith Raj, K. Ezhilarasan, K. Karthik Reddy, Chaithrashree Harish |
10.5120/ijca2023922865
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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 },
year = { 2023 },
volume = { 185 },
number = { 16 },
pages = { 40-43 },
doi = { 10.5120/ijca2023922865 },
publisher = { Foundation of Computer Science (FCS), NY, USA }
}
%0 Journal Article
%D 2023
%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%T
%J International Journal of Computer Applications
%V 185
%N 16
%P 40-43
%R 10.5120/ijca2023922865
%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
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