Providing sustainable living through early detection of metabolic syndrome

The non-healthy lifestyle of the people in developed countries is one of the reasons for higher amounts of atherosclerosis and diabetes type 2, which are related to a metabolic syndrome. This paper proposes an intelligent system for the early, unobtrusive detection of metabolic syndrome in order to support sustainable environments for today’s and tomorrow’s generations. An implementation of Fuzzy ARTMAP Neural Network for diagnosis of Metabolic Syndrome is presented. It allows classifying H NMR serum spectra into five classes, from healthy person to person with Metabolic Syndrome. Using “Voting strategy†it gains an ability to classify samples with a confidence value

Comparison of Two Ambient Intelligence Approaches to Elderly Care

There is more and more elderly in the developed countries and not enough younger people to take care of them. We are presenting a semantic ambient media system for health-care monitoring to allow quality and safe living of elderly at their homes instead of needing them to go to nursing homes, which are overcrowded. Moreover, their offspring would not be overwhelmed with care for the elderly. The study illustrates two ambient intelligence approaches to the elderly care, both in the sense of four concepts of the semantic ambient media

An Unobtrusive Semantic Health-Monitoring Medium

We present a generalized data mining approach to the detection of health problems and falls in the elderly for the purpose of prolonging their autonomous living. The input for the data mining algorithm is the output of the motion-capture system. The approach is general since it uses a k-nearestneighbor algorithm and dynamic time warping with the time series of all the measurable joint angles for the attributes instead of a more specific approach with medically defined attributes. Even though the presented approach is more general and can be used to differentiate other types of activities or health problems, it achieves very high classification accuracies, similar to the more specific approaches described in the literature

Generalized approach to prolonging of autonomous living of elderly with semantic ambient media

This paper is presenting generalized approach to detection of health problems and falls of the elderly for the purpose of prolonging autonomous living of elderly using semantic ambient media. The movement of the user is captured with the motion capture system, which consists of the tags attached to the body, whose coordinates are acquired by the sensors situated in the apartment. Output time-series of coordinates are modeled with the proposed data mining approach in order to recognize the specific health problem or fall. The approach is general in a sense that it uses k-nearest neighbor algorithm and dynamic time warping with time-series of all measurable joint angles for the attributes instead of the more specific approach with medically defined attributes. It is two-step approach; in the first step it classifies person's activities into five activities including different types of falls. In the second step it classifies walking patterns into five different health states; one healthy and four unhealthy. Even though the new approach is more general and can be used to differentiate also from other types of activities or health problems, it achieves very high classification accuracies, similar to the more specific approach

An Intelligent System for Prolonging Independent Living of Elderly

The number of elderly people is constantly increasing in the developed countries. Elderly tend to lead an isolated life away from their offspring; however, they may fear being unable to obtain help if they are injured or ill. During the last decades, this fear has generated research attempts to find assistive technologies for making living of elderly people at homes easier and independent, as is the aim of this research work. Research study proposes a generalized approach to an intelligent and ubiquitous care system to recognize a few of the most common and important health problems of the elderly, which can be detected by analyzing their movement. In the event that the system was to recognize a health problem, it would automatically notify a physician with an included explanation of the automatic diagnosis. It is two-step approach; in the first step it classifies person's activities into five activities: fall, unconscious fall, walking, standing/sitting, lying down/lying. In the second step, it classifies walking patterns into five different health states; one healthy and four unhealthy: hemiplegia (usually the result of stroke), Parkinson’s disease, leg pain and back pain. Moreover, since elderly having these health problems are less stable and more prone to falls, recognizing them leads not only to detection but indirectly also to prevention of falls of elderly people. In the initial approach movement of the user is captured with the motion capture system, which consists of the tags attached to the body, whose coordinates are acquired by the sensors situated in the apartment. In the current approach wearable inertial sensors are used, allowing monitoring inside or outside of the buildings. Output time-series of coordinates are modeled with the proposed data mining approach to recognize the specific health problem