Integrated monitoring system for fall detection in elderly

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

Automated Monitoring System for Fall Detection in the Elderly

Falls are a major problem for the elderly people living independently. Accordingto the World Health Organization, falls and sustained injuries are the third cause of chronic disability. In the last years there have been many commercial solutions aimed at automatic and non automatic detection of falls like the social alarm (wrist watch with a button that is activated by the subject in case of a fall event), and the wearable fall detectors that are based on combinations of accelerometers and tilt sensors. Critical problems are associated with those solutions like button is often unreachable after the fall, wearable devices produce many false alarms and old people tend to forget wearing them frequently. To solve these problems, we propose an automated monitoring that will detects the face of the person, extract features such as speed and determines if a human fall has occurred. An alarm is triggered immediately upon detection of a fall

Real Time Recognition of Elderly Daily Activity using Fuzzy Logic through Fusion of Motion and Location Data

One of the major problems that may encounter old people at home is falling. Approximately, one of three adults of the age of 65 or older falls every year. The World Health Organization reports that injuries due to falls are the third most common cause of chronic disability. In this paper, we proposed an approach to indoor human daily activity recognition, which combines motion and location data by using a webcam system, with a particular interest to the problem of fall detection. The proposed system identifies the face and the body in a given area, collects motion data such as face and body speeds and location data such as center of mass and aspect ratio; then the extracted parameters will be fed to a Fuzzy logic classifier that classify the fall event in two classes: fall and not fall

Implementation of Intelligent Monitoring System for year Elderly

One of three adults 65 or older falls every year. Injuries sustained among the elderly because of falls are a major problem worldwide. Demographic predictions of population aged 65 and over suggest the need for telemedicine applications in the eldercare domain. In this paper, we propose an intelligent surveillance system that monitors human activities with a particular interest of the problem of fall detection. To make the motion detection and object tracking fully automatic and robust under different illumination conditions, combination of best-fit approximated ellipse around the face and temporal changes of head position, would provide a useful cue for detection of different behaviors. The system identifies the face, collects data suchas speed of movement, and triggers an alarm on the determination of fall event. Reliable recognition rate of experimental results underlines satisfactory performance of our system

Implementation of a monitoring system for fall detection in elderly healthcare

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

A survey of methods for the construction of a Brain Computer Interface

A brain computer interface is an artificial intelligence system that provides the brain with a way to communicate with the outside environment. This paper aims to provide guidelines for biomedical researchers by discussing each phase of the construction of a Brain Computer Interface. It explains invasive and noninvasive BCI. Then, it presents the most commons methods employed for designing a BCI and discusses the artifacts removal. Finally, this paper summarizes the advantages and disadvantages of the presented methods and discusses the future steps into this field of research

Electroencephalographic based brain computer interface for unspoken speech

This paper presents a Brain Computer Interface methodology for unspoken speech recognition based on Electroencephalography (EEG). Each phase within this approach is presented and discussed, followed by the noise elimination methodology and ends up by features extraction and data classification. The presented work consists of database construction with features vectors that will be classified into different classes by applying an articial neural network with three layers. The proposed approach provides results with high percentage of recognition (93% Testing, 95% Validation) when applied on two English words ON/OFF acquired from 2 different resources