An Adaptive Decision Support System for Individual Persons (ADSIP) Suffering from Chronic Diseases

The demographic development changes Germany as well as China. In 2035, Germany will be one of the countries that have the oldest population in the world, more than half of the population will be 50 years or older. China is facing the similar problem. The population size of elderly people will be more than 400 million in 2050, accounting for 30% of the total population. This progressive ageing of the population is leading to an increase in the proportion of people with disabilities, mental diseases, and chronic illnesses, resulting in new demands placed on healthcare and welfare system in both countries. Ambient Assisted Living (AAL) is a new concept that is supposed to deal with this demographic change. The concept of AAL is to extend the time elderly people can live in their preferred environment by increasing their autonomy, self-confidence, mobility and safety. One important method of resolution in AAL is using wearable computing technologies to provide unobtrusive in-home monitoring and communication services for elderly people, so that their health and functional capability can be maintained for a long time on the one hand, and a better and healthier lifestyle can be promoted on the other hand. In this way, substantial amounts of funding of healthcare and welfare systems can be saved as well.

The Sino-German cooperation project HealthWear@AAL aims to develop an intelligent wearable solution for in-home health monitoring and diagnosing, addressing elderly people with chronic health conditions. The proposed platform consists of wearable computerized clothing that integrates low-cost and energy efficient sensors for vital signs and movements as well as physical activities monitoring, and an adaptive decision support system for individual persons (ADSIP) with the purpose of reliable health status diagnosis in real time. The proposed wearable solution will be initially applied to people suffering from COPD (Chronic Obstructive Pulmonary Disease).

It is expected that with the adaptive decision support system ADSIP the measured deviation from the “normality” can be diagnosed either as a real critical situation or as a normal uncritical variation, depending on an individual person’s health characteristics, and the potential exacerbation risks can be predicted in an early phase. As a consequence, the performance of in-home monitoring for COPD patients will be improved through increasing the robustness and reliability of the decision support system.

The proposed approach to achieve reliable health status diagnosis for the current context is to individualize the decision support system by means of learning the person’s individual health characteristics from continuously or periodically obtained sensor data, so that an adaptive decision-theoretic model can gradually adjust its inference to the individual's health conditions.

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