Institute for
Robotics and Process Control

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Intelligent Room

Decription of project

At our institute we are developing new visual monitoring systems for elderly persons. Our long-term aim is a monitoring system that allows elderly person to live a long, independent and secure life in their home environment. The monitoring system do not care about nursing tasks, instead the daily life is affected as less as possible ensuring the health of the person. Using cameras and image processing techniques have the advantage of being invisible for the user. Moreover, no interaction between the user and the system is needed.

A first version of the system is already runnable and currently tested in a real home environment. This version uses a fish-eye camera mounted at the ceiling of the room and can automatically detect falls. When a fall is detected the system can make an emergency call. We are using different model-based and modelfree approaches for the tracking and the recognition of falls. The following figures illustrate a blob-based approach, where the different body parts are modeled as color blobs.

Standing person

Lying person

Modelfree Approach (Windows Media, 5.2 MB)
Blob-based Approach (Windows Media, 2.3 MB)

Using fish-eye cameras have the advantage of mapping the whole room onto just one image. A single pinhole-like or pan-tilt-zoom camera can only map a part of the room.

The following german television shows have reported about our visual emergency:
ARD-Sendung 25.02.2007 (Windows Media, 79.3 MB)
NDR-Sendung 11.08.2008 (Windows Media, 104.1 MB)

To detect falls at night we are integrating active approaches. Infra-red lights are mounted at different locations in the room, preferred at the ceiling. The shadow information is used to distinguish between a standing and a lying person. As can be seen in the following figures the shadow of a standing person is much bigger than the one of a lying person:

Shadow simulation of a lying person

Shadow simulation of a standing person

Till now we just consider fall detection, but of course fall prevention is another challenging task. Changes of the gait can be due to diseases and can result in a fall. Visual fall prevention allows the detection of these changes and e.g. the notification of the general practitioner. First results were presented in the german television show ARD-Ratgeber-Technik (April 2008).
ARD-Sendung (Windows Media, 69.7 MB)
This work has been supported by the Deutsche Telekom which is kindly acknowledged.


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