KImAge

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Seeing ageing through different eyes

AI-supported systematisation of images of old age in central lifeworlds

We all have images of old age, i.e. ideas about what it means to grow older or to be old. Age images play an important role for us and our society. They determine our thoughts and actions, the decisions we make, our development and health and even our life expectancy.

The KImAge project aims to clarify the structure and relative importance of age images in the everyday lives of different age and population groups. Using a photography-based method, images of old age in various everyday environments (general population, working world, healthcare) are surveyed by adults of all age groups. A specifically trained AI model will help to sort the extensive photographs in order to recognise and evaluate systematics. In this way, we hope to gain urgently needed clues as to how the diversity of ageing can be made more visible.

  • Images of old age in the truest sense of the word We already know quite a lot about the conscious parts of images of old age. The photographs are primarily intended to capture the unconscious, emotional parts - "A picture is worth a 1,000 words."
  • Images of old age in everyday life Images of age are omnipresent. In healthcare and the world of work, the (in)equal treatment of people on the basis of age is particularly critical. We gain insights here through strong partnerships.
  • Images of age across the lifespan Our ideas about ageing, being old and old people are shaped from early childhood. For a long time, they are hardly relevant to ourselves. This changes with increasing age.
  • Artificial intelligence for image analysis With the support of an AI model, the large volumes of images are to be analysed on a representative scale. This opens up innovative possibilities for research.

Project partners

  • Prof. Dr. Christophe Kunze, Assistive Healthcare Technologies
  • Prof. Dr. Christoph Reich, Computer Science
  • Prof. Dr. Kirsten Steinhausen, Applied Health Sciences
  • Prof. Dr. Peter König, Nursing and Rehabilitation Management
  • Prof. Dr. Gerald Ackner, Applied Cognitive Psychology
  • Prof. Dr. Jochen Huber, Applied Computer Science

Funding

The project is supported by the Carl Zeiss Foundation.

 

  • KImAge (I4703)

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