A Survey On Efficient Human Fall Detection System

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.

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.

Various fall-detection solutions have been previously proposed to create a reliable surveillance system for elderly people with high requirements on accuracy, sensitivity and specificity. In this paper, an enhanced fall detection system is proposed for elderly person monitoring that is based on smart sensors worn on the body and operating through consumer home networks. With treble thresholds, accidental falls can be detected in the home healthcare environment. By utilizing information gathered from an accelerometer, cardiotachometer and smart sensors, the impacts of falls can be logged and distinguished from normal daily activities.

2018

According to a report published by World Health Organization, early fall detection is an active problem in the old age group people. The report revealed that the fall detection problem affect 28-35% people for the people around 65 years of age and 32-45% for those over 70 years. In the past few years, various research studies have been conducted on the fall detection problem. Objective of these research studies is to develop a system to provide a timely medical assistance to an old age person in case of fall. These studies have further suggested that early detection of fall could not only minimize the damage in terms of head, spinal or any similar major bone injuries but also provide timely assistance thereby saving efforts and money. This paper presents a survey of various fall detection systems and methods.. Most commonly used approaches in fall detection system are wearable, ambience and camera based device.

Handbook of Research on ICTs for Human-Centered Healthcare and Social Care Services, 2013

Statistics show that, each year, falls affect tens of millions of elderly people throughout the world. Falls are the leading cause of injury deaths and injury-related hospitalization among people over 65 years old. A system able to automatically detect falls could be an important tool from a social point of view, as it would contribute to the prompt assistance of these emergency cases. Currently, many researchers are interested in the development of fall detection systems. This chapter presents several approaches suggested so far, with special attention to the different strategies and technologies applied. Other sensor technologies that could be applied to this field are also referred. Additionally, a new fall detection system based on a machine learning paradigm, using neural networks, is suggested. The system was trained using fall and non-fall examples. Although the test set has included some particular examples, problematic for detection of the correspondent motion, the obtained results present good specificity (88.9%) and sensitivity (93.9%) rates.

2016

Falling down is among the major causes of medical problem that are faced by the elderly people. Elderly people tend to injured themselves from falling down more often especially when they are living alone. When a falling event occurred, medical attention need to be provided immediately in order to reduce the risk of faller from getting severe injuries which may lead to death. Several technologies have been developed which some utilized webcams to monitor the activities of elderly people. However, the cost of operation and installation is expensive and only applicable for indoor environment. Some user also worried about their privacy issues. Current commercialized device required user to wear wireless emergency transmitter in form of pendant and wristband. This method will restrict the user movement and produce high false alarm due to frequent swinging and movement of the device. This project proposed a fall detection system which is cost effective and reliable to detect fall and ale...

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.

Falling is a common accident and the most significant cause of injury for elderly person. This study investigates the methodology to identify falls from normal Activities of Daily Living (ADLs). In this study, a wireless sensor system (WSS), based on accelerometer and gyroscope, is placed at the centre of the chest to collect real-time fall data. The WSS contains a set of ADXL345 (3-axis digital accelerometer sensor), ITG3200 (3-axis digital gyroscope sensor), MCU LPC17680 (ARM 32-bit cortex M3), and Wi-Fi module RN13. Experiment protocols consisting of four types of falls such as forward fall, backward fall, and side way fall (left and right) along with normal gait involved 324 tests on 18 human subjects. The results from the experiment shows the system and algorithm could distinguish between falling and ADLs with an accuracy of 99.382%.

Signal and Information Processing Association Annual Summit and Conference (APSIPA), 2014 Asia-Pacific, 2014

An unintentional fall can make injure to elderly. This paper aims to develop a portable and efficient device to monitor the falls in the elderly population by integrating a micro controller, a 3-DOF acceleration sensor, a GSM/GPRS modem and corresponding embedded fall detection algorithms. This system can work well in both indoor and outdoor environments. The human activities can be sensed by the low-cost and low power 3-DOF accelerometer. The acceleration signals are brought to the micro controller to monitor and alert the fall events. The cascade posture recognitions are proposed to enhance the fall detection accuracy by determining if the posture is a result of a fall. If the people fall, an alert message would be sent to their relative or a health center via the GSM/GPRS modem.

IRJET, 2021

Falls are a serious health concern for the elderly who live in the neighborhood. For more than two decades, medical institutions have conducted substantial research on falls in order to attenuate their impact (e.g., loss of freedom, fear of falling, etc.) and minimize their consequences (e.g., Cost of hospitalization, etc.). However, the subject of elderly people falling has piqued the interest of scientists as well as health experts. Indeed, falls have been the subject of several scientific investigations as well as the aim of numerous commercial products from academia and industry. These studies have addressed the issue by employing fall detection algorithms that have exhausted a range of sensor methods. Recently, researchers have moved their focus to fall prevention, with the goal of detecting falls before they occur. The chief contribution of this study in this matter is to offer a thorough outline of elderly falls and to recommend an all-purpose solution of fall-related systems based on real time posture estimation. Based on this common ground classification, an extensive research plan ranging from fall detection to fall prevention technologies was also carried out. Data processing techniques are also featured in both the fall detection and fall prevention courses. The goal of this effort is to provide medical technologists in the field of public health with a good understanding of fall-related systems.