Design and Evaluation of a Fall Detection Algorithm on Mobile Phone Platform

2011 ◽  
pp. 28-35 ◽  
Author(s):  
Manuel Silva ◽  
Pedro M. Teixeira ◽  
Filipe Abrantes ◽  
Filipe Sousa
Keyword(s):  
Mobile Phone ◽  
Fall Detection ◽  
Detection Algorithm

DEVELOPMENT OF FALL DETECTION ALGORITHM FOR TELEMONITORING SYSTEM

2019 ◽  
Vol 7 (2) ◽  
pp. 1
Author(s):  
C. A. MEBRIM ◽  
O. C. UBADIKE ◽  
A. M. AIBINU ◽  
I. I. ALEGBELEYE ◽  
A. J. ONUMANYI ◽  
...  
Keyword(s):  
Fall Detection ◽  
Detection Algorithm

scholarly journals Human Fall Detection Algorithm Design Based on Sensor Fusion and Multi-threshold Comprehensive Judgment

2020 ◽  
Vol 32 (4) ◽  
pp. 1209 ◽  
Author(s):  
Junsuo Qu ◽  
Chen Wu ◽  
Qian Li ◽  
Ting Wang ◽  
Abdel Hamid Soliman
Keyword(s):  
Sensor Fusion ◽  
Algorithm Design ◽  
Fall Detection ◽  
Detection Algorithm ◽  
Comprehensive Judgment

scholarly journals Implementation and Application of Fall Detection on Mobile Perception Robot

2021 ◽  
Vol 2136 (1) ◽  
pp. 012053
Author(s):  
Zeyu Chen
Keyword(s):  
Elderly Population ◽  
Fall Detection ◽  
The Elderly ◽  
Detection Algorithm ◽  
Data Sets ◽  
Construction Technology ◽  
Robot Operating System ◽  
Map Construction ◽  
Robot Perception ◽  
Window Method

Abstract With the rapid increase in the number of people living in the elderly population, reducing and dealing with the problem of falls in the elderly has become the focus of research for decades. It is impossible to completely eliminate falls in daily life and activities. Detecting a fall in time can protect the elderly from injury as much as possible. This article uses the Turtlebot robot and the ROS robot operating system, combined with simultaneous positioning and map construction technology, Monte Carlo positioning, A* path planning, dynamic window method, and indoor map navigation. The YOLO network is trained using the stance and fall data sets, and the YOLOv4 target detection algorithm is combined with the robot perception algorithm to finally achieve fall detection on the turtlebot robot, and use the average precision, precision, recall and other indicators to measure.

Fall Detection Algorithm using Body Angle for Accurate Classification of Falls and ADLs

2021 ◽  
Author(s):  
Ali Ibrahim ◽  
Kabalan Chaccour ◽  
Georges Badr ◽  
Amir Hajjam El Hassani
Keyword(s):  
Fall Detection ◽  
Detection Algorithm ◽  
Body Angle

Research and Implementation of Two-Layer Fall Detection Algorithm

2018 ◽  
Author(s):  
Gao Tongyue ◽  
Yang Jia ◽  
Huang Kaida ◽  
Hu Qingxuan ◽  
Zhao Fengshou
Keyword(s):  
Fall Detection ◽  
Detection Algorithm

Development of a Fall Detection Algorithm for Powered Two Wheelers Application

2014 ◽  
Author(s):  
Federico Giovannini ◽  
Niccolò Baldanzini ◽  
Marco Pierini
Keyword(s):  
Fall Detection ◽  
Detection Algorithm

Accelerometer Sensor Based Fall Sensing for Elderly

2015 ◽  
Vol 73 (1) ◽  
Author(s):  
Anas Mohd Noor ◽  
Hafizudin Zainudin ◽  
Normaheran Hanafi ◽  
Siti Aishah Baharuddin ◽  
Mohamad Aliff Abdul Rahim
Keyword(s):  
Mobile Communications ◽  
Signal Transmission ◽  
Cost Effective ◽  
Fall Detection ◽  
Detection Algorithm ◽  
Prototype System ◽  
Accelerometer Sensor ◽  
Wireless Signal ◽  
Overall Performance ◽  
Key Parameter

Fall can be recognized as an abnormal or action of losing an upright motion which will cause people especially elderly to suffer from pain and more seriously can affect one’s health. Being able to detect fall is key parameter to decrease the risk of severe injury to the seniors. There are such existing fall detection products on the market to assist elderly so that immediate response could be taken. However, due to complexity system, high cost and employing outside technology, these products initiate limitations such as maintenance and system enhancement. In this project, a fall detection device and system is developed using local technology, simple and cost effective. The prototype system consist of accelerometer sensing circuit, microcontroller with wireless signal transmission, Global System for Mobile Communications (GSM) notification alert for mobile phone and graphical user interface (GUI) to obtain real-time monitoring. The simple fall detection algorithm is developed to ensure false detection could be minimized. The overall performance of the developed device and system is proven reliable and practical. 

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