AAL - Interface

Europe has one of the highest shares of elderly people in the world. In 2016, already 19% of the European population was 65 years and over. Looking at the prognoses, this share will increase to 29% by 2070; meaning that more than 1 in 4 people in Europe will be 65 years or over. As people grow older, it is expected that there will be more people with age-related chronic diseases and in need of long-term care.

Structural changes in population ratios will have serious and unpredictable consequences, among them the already slow-onset crisis of caregiving shortages and the danger of a precipitous decline in social welfare.

The concept of AAL

Ambient Intelligence (AmI) is a research paradigm that brings intelligence to everyday environments through sensor networks, pervasive computing and artificial intelligence. This way, our environments become sensitive, adaptive and responsive to our presence and needs.

Wearable devices for gesture monitoring

Monitoring the posture can get many extraordinary results to assist people at home. For instance, lying down at a specific time can infer the user’s sleep time and sleep quality; sudden posture changes combined with heart rate changes can infer whether the user is in a dangerous situation. In this particular project, posture monitoring will be used as the primary data input source to automatically calculate the activity information and measurable data such as heart rate. Wearable smart bracelets will be the main input device, and the patch-type motion sensor sticks on the limbs to calculate comparable data.

Data comparison

In a practical application of the AAL concept, the data would be isolated locally. They will be compared to data such as posture obtained a day or weeks ago to determine potential chronic disease features or possible health hazards; constructive comments or critical information will then be passed to the hospital. The integrated dataset on the left shows real-time health risks and weekly statistics in a dot matrix format. Users can click to get detailed health reports for a specified time frame, while doctors and nurses analyze the data to provide an objective basis for diagnosis.

Explicit output

For the elderly target audience, we reduced the complexity of interaction as much as possible, with as little interactive language and intuitive data presentation as possible. Therefore, in the actual interface design, we clearly use the most direct text elements to describe the key data processing results.

Intuitive infographic

On the left side of the screen, the point clouds animations point out posture changes intuitively. Through the posture change trend for consecutive days, the potential risk areas are clearly marked in red. And it is worth to mention that the information will not be displayed in the user’s interface for those predictions of serious health condition. It will be directly submitted to relatives or personal doctors to prevent panic.

Conclusion - Design metrics

The AAL concept aims to predict early abnormalities and diagnoses to save care resources and improve the quality of care. However, the product cannot essentially solve the problem of elderly care services. The lack of elderly care results from the increase in the budget of the human resources department and, more importantly, the rapid development of contemporary society and the gradual alienation of relatives, which is declared in the Connecting project.