A Threshold Algorithm in a Fall Alert System for Elderly People (original) (raw)

VitaFALL: Advanced Multi-Threshold Based Reliable Fall Detection System

Recent Advances in Computer Science and Communications, 2022

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. So even for the daily activity in the life of aged people, an automatically fall detecting system and vital signs examining system 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 triple-axis accelerometer, and Vital Signs Paramet...

Development of Fall Risk Detector for Elderly

TELKOMNIKA Telecommunication Computing Electronics and Control, 2018

In Malaysia, falls has become the most common injuries for elderly. Therefore, a wearable fall detector device is created to decrease the risk of serious injury among elderly. The device consists of an accelerometer (ADXL345) as a sensor, an Arduino Nano as a microcontroller, and a Global System for Mobile Communications (GSM) as a notifier. A group of 15 young people participated in performing several sets of different falls and ADL (daily life activities) to determine the ability of the device. The result shows a good functioning performance by 92.6% sensitivity to detect fall and 89.3% specificity in discriminate fall from daily life activity.

Review of Fall Detection and Alert Systems for Elderly People

IJERT, 2021

Nowadays since people are busy due to their schedule, it's not always possible to keep someone at home to take care of elder person. Most of the people who have fallen cannot get up without assistance. Tissue injuries, joint dislocations, bone fractures ,and head trauma are some of the damages caused by falling. The absence of movement of a person after a fall may cause severe complications regarding health and may even lead to death if immediate assistance is not provided. Fall detection system using sensors are available in the market. But they need to be attached to the body to detect fall. The elderly may forget to wear them and they can cause discomfort too. In order to overcome these challenges, automatic fall detection and alert system can be used at the home for quicker assistance. The solutions in these papers are implemented using Machine Learning, Deep Learning and Computer Vision technology. In this paper, we discuss different methodologies to detect human falls. This paper is aimed towards analyzing the effectiveness of those methods for the detection of human falls.

A Dynamic Evidential Fall Monitoring and Detection System for Elder Persons

International Journal of Science Technology & Engineering

The mobile application is capable of detecting possible falls for elderly, through the use of special sensors. The alert messages contain useful information about the people in danger, such as his/her geo location and also corresponding directions on a map. In occasions of false alerts, the supervised person is given the ability to estimate the value of importance of a possible alert and to stop it before being transmitted. The system is capable of monitoring ELDERLY PEOPLE in real time. The system, including calibration of accelerometers and measurement is explained in detail. This fall detection system is designed to detect the accidental fall of the elderly and alert the carers or their loved ones via Smart-Messaging Services (SMS) immediately. This fall detection is created using microcontroller technology as the heart of the system, the accelerometer as to detect the sudden movement or fall and the Global System for Mobile (GSM) modem, to send out SMS to the care taker.

Increased Fall Detection Accuracy in an Accelerometer-Based Algorithm Considering Residual Movement

Every year over 11 million falls are registered. Falls play a critical role in the deterioration of the health of the elderly and the subsequent need of care. This paper presents a fall detection system running on a smartwatch (F2D). Data from the accelerometer is collected, passing through an adaptive threshold-based algorithm which detects patterns corresponding to a fall. A decision module takes into account the residual movement of the user, matching a detected fall pattern to an actual fall. Unlike traditional systems which require a base station and an alarm central, F2D works completely independently. To the best of our knowledge, this is the first fall detection system which works on a smartwatch, being less stigmatizing for the end user. The fall detection algorithm has been tested by Fondation Suisse pour les Téléthèses (FST), the project partner for the commercialization of our system. Taking advantage of their experience with the end users, we are confident that F2D meets the demands of a reliable and easily extensible system. This paper highlights the innovative algorithm which takes into account residual movement to increase the fall detection accuracy and summarizes the architecture and the implementation of the fall detection system.

IJERT-Review of Fall Detection and Alert Systems for Elderly People

International Journal of Engineering Research and Technology (IJERT), 2021

https://www.ijert.org/review-of-fall-detection-and-alert-systems-for-elderly-people https://www.ijert.org/research/review-of-fall-detection-and-alert-systems-for-elderly-people-IJERTV10IS050358.pdf Nowadays since people are busy due to their schedule, it's not always possible to keep someone at home to take care of elder person. Most of the people who have fallen cannot get up without assistance. Tissue injuries, joint dislocations, bone fractures ,and head trauma are some of the damages caused by falling. The absence of movement of a person after a fall may cause severe complications regarding health and may even lead to death if immediate assistance is not provided. Fall detection system using sensors are available in the market. But they need to be attached to the body to detect fall. The elderly may forget to wear them and they can cause discomfort too. In order to overcome these challenges, automatic fall detection and alert system can be used at the home for quicker assistance. The solutions in these papers are implemented using Machine Learning, Deep Learning and Computer Vision technology. In this paper, we discuss different methodologies to detect human falls. This paper is aimed towards analyzing the effectiveness of those methods for the detection of human falls.