Automatic fall detection and activity monitoring for elderly

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.

Procedia Computer Science, 2011

Falls are a major problem for elderly living independently. The World Health Organization reports that injuries due to falls are the third most common cause of chronic disability. We propose an automated monitoring system that identifies faces in a given area, collects data such as speed of movement, and triggers an alarm if these data suggest the person has fallen. Our system does not suffer the problems with the existing commercial devices such as social alarms, e.g., a wristwatch with a button that must be activated by the subject and wearable fall detectors comprising accelerometers and tilt sensors.

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.

Expert Systems with Applications, 2013

The Homecare project, which is part of a research project funded by the French National Research Agency (ANR), aims to define a new multi-sensor monitoring system for the elderly with cognitive disabilities in a care unit. Two subjects were recruited to participate to experimental trials. The main objective of this project is to design and test a complete monitoring system at a real site. It is a new clinical and technical approach which is complex to implement: Homecare is intended to propose a possible technical solution, demonstrate its feasibility and illustrate its use working at a protected site. The system consists of a motion sensor network deployed on the ceiling to monitor motion and an electronic patch worn by the subjects to identify them and detect falls. In order to locate tagged subjects inside the care unit, a network of anchor points is used. From these positions and movement data, an analysis algorithm detects an abnormal situation and alerts the nursing staff in real time. A Web application allows the medical staff to access movements and alarms. The complete monitoring system has been functioning for several months and continuously monitors two patients around the clock. In this paper, we present the implementation of the system, the method of localization inside the care unit, and the characterization of the fall detector, and we show certain results relating to activity data.

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.

Since many elderly have to live alone, they might suffer from depression, which can lead to physically disabilities. One cause of physical disabilities is falling. Falls are a serious issue for the elderly and can lead to injuries, paralysis or even death. To provide safety and comfort for senior citizens, we adapted the intelligent space concept to design and build a prototype of our intelligent space for them. Our intelligent space provides three services: emergency detection, medical consultation and long-distance social interaction services. We design and proposed an implementation of a fall detection function using 3D acceleration sensor to detect the elderly falls and request emergency service in our intelligent space.

2015

With the dramatically increasing of world population, the caring of elderly person becomes a globally concerned problem. The old want an independent living space and their family members need to work, so an intelligent old person surveillance system is in urgent need. An accidental fall detection system for elderly is constructed to detect whether an old person has fallen down and inform his or her family members through public mobile service, so as to get the elderly helped in time. This fall detection system 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 message to the receiver without any participation of the person who needs help. The system designed can detect the accidental fall of people accurately and send a message originally edited to supposed family members. And it can be furthered used in a broad application field. This report cove...

IRJET, 2023

Falls by elderly individuals and patients could be dangerous if not caught in time. The idea is to create a fall detection system that, in the event of an emergency, sends an SMS to the involved parties or to the doctor. Continuous monitoring of patients who are unwell and prone to falling is required to reduce falls and the harm they cause. The suggested solution involves creating a prototype of an electronic device that is used to detect falls in older people and those who are at risk for them. In this article, the change in acceleration in three axes-measured using an accelerometer-is used to determine the body position. To measure the tilt angle, the sensor is positioned on the lumbar area. To minimise false alarms, the acceleration values for each axis are compared twice with a threshold and a 20second delay between comparisons. The threshold voltage values are chosen using experimental techniques. Microcontrollers are used to carry out the algorithm. The GPS receiver, which is configured to track the subject continually, pinpoints the position of the fall. When a fall is detected, the gadget communicates by sending a text message via a GSM modem

Falling and its resulting injuries are an important public health problem for older adults. The National Safety Council estimates that persons over the age of 65 have the highest mortality rate (death rate) from injuries. The risk of falling increases with age; one of three adults 65 or older falls every year. Demographic predictions of population aged 65 and over suggest the need for telemedicine applications in the eldercare domain. This paper presents an integrated monitoring system for the detection of people falls in home environment. The system consist of combining low level features extracted from a video and heart rate tracking in order to classify the fall event. The extracted data will be processed by a neural network for classifying the events in two classes: fall and not fall. Reliable recognition rate of experimental results underlines satisfactory performance of our system

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.