fall detection system

Expert Systems with Applications, 2012

Accidental falls of our elderly, and physical injuries resulting, represent a major health and economic problem. Falls are the most common cause of serious injuries and are a major health threat in the stratum of older population. Early detection of a fall is a key factor when trying to provide adequate care to elderly person who has suffered an accident at home. Therefore, the detection of falls in the elderly remains a major challenge in the field of public health. Specific actions aimed at the fall detection can provide urgent care which allows, on the other hand, drastically reduce the cost of medical care, and improve primary care service. In this paper, we present a support system for detecting falls of an elder person by the combination of a wearable wireless sensor node based on an accelerometer and a static wireless nonintrusive sensory infrastructure based on heterogeneous sensor nodes. This previous infrastructure called DIA (Dispositivo Inteligente de Alarma, in Spanish) is an AAL (Ambient Assisted Living) system that allows to infer a potential fall. This inference is reinforced for prompt attention by a specific sensorisation at portable node sensor in order to help distinguish between falls and daily activities of assisted person. The wearable node will not determine a falling situation, it will advice the reasoner layer about specific acceleration patterns that could, eventually, imply a falling. Is at the higher layer where the falling is determined from the whole context produced by mesh of fixed nodes. Experimental results have shown that the proposed system obtains high reliability and sensitivity in the detection of the fall.

IEEE Pervasive Computing, 2000

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

ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal

In this work, a distributed system for fall detection is presented. The proposed system was designed to monitor activities of the daily living of elderly people and to inform the caregivers when a falls event occurs. This system uses a scalable wireless sensor networks to collect the data and transmit it to a control center. Also, an intelligent algorithm is used to process the data collected by the sensor networks and calculate if an event is, or not, a fall. A statistical method is used to improve this algorithm and to reduce false positives. The system presented has the capability to learn with past events and to adapt is behavior with new information collected from the monitored elders. The results obtained show that the system has an accuracy above 98%.

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.

The aging population represents an emerging challenge for healthcare since elderly people frequently suffer from chronic diseases requiring continuous medical care and monitoring. Sensor networks are possible enabling technologies for ambient assisted living solutions helping elderly people to be independent and to feel more secure. This paper presents a multi-sensor system for the detection of people falls in home environment. Two kinds of sensors are used: a wearable wireless accelerometer with onboard fall detection algorithms and a time-of-flight camera. A coordinator node receives data from the two sub-sensory systems with their associated level of confidence and, on the basis of a data fusion logic, it operates the validation and correlation among the two sub-systems delivered data in order to rise overall system performance with respect to each single sensor sub-system. Achieved results show the effectiveness of the suggested multi-sensor approach for improving fall detection service in ambient assisted living contexts.

Our modern societies are suffering the increase of elderly population while at the same time social security and health costs must be cut down. In order to avoid the need of special care centers, the actual trend is to encourage elderly to stay living autonomously in their own homes as long as possible. The product presented in this paper contributes to this objective, since it provides user localization, automatic fall detection and activity monitoring both for indoors and outdoors activities, associated to a complete call centre for medical monitoring of the patient as well as to provide support and manage emergency situations.

2008

This paper presents a hardware and software framework for reliable fall detection in the home environment, with particular focus on the protection and assistance to the elderly. The integrated proto- type includes three difierent sensors: a 3D Time-Of-Flight range camera, a wearable MEMS accelerometer and a microphone. These devices are connected with custom interface circuits to a central PC that

2010

The problem of fall detection in elderly patients is particularly critical in persons who live alone or are alone most of the day. The use of information and communication technologies to facilitate their autonomy is a clear example of how technological advances can improve the quality of life of dependent people. This article presents a prototype developed with a low cost device (the gamepad of a known video console) using its Bluetooth communication capabilities and built-in accelerometer. The latter is much more sensitive than other similar devices integrated in mobile phones and much cheaper than industrial accelerometers. Besides its stand-alone use, the system can be connected to a generic remote monitoring system that has been developed as a software product line for use in aged people's residences.

The Gerontologist, 2015

Falls are a major problem for the elderly people leading to injury, disability, and even death. An unobtrusive, in-home sensor system that continuously monitors older adults for fall risk and detects falls could revolutionize fall prevention and care. A fall risk and detection system was developed and installed in the apartments of 19 older adults at a senior living facility. The system includes pulse-Doppler radar, a Microsoft Kinect, and 2 web cameras. To collect data for comparison with sensor data and for algorithm development, stunt actors performed falls in participants' apartments each month for 2 years and participants completed fall risk assessments (FRAs) using clinically valid, standardized instruments. The FRAs were scored by clinicians and recorded by the sensing modalities. Participants' gait parameters were measured as they walked on a GAITRite mat. These data were used as ground truth, objective data to use in algorithm development and to compare with radar a...