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 How Wearable Cameras Could Help Diagnose Dementia

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Date posted: 08/08/2010

One of the problems with " life recorders", wearable video cameras that record your every movement, is making sense of the huge datasets they produce. Given a day's worth of inane footage--get up, wander into bathroom, brush teeth etc--what can you usefully do with it?

Various groups have proposed that such cameras could make excellent aide memoires, immediately locating lost car keys and remembering old faces. They'd also allow you to relive memories with otherwise forgotten detail--what was she wearing the day we met?

That's all well and good but there is a serious practical problem. How can a computer make sense of the endless stream of footage?

Scene change detection is relatively straight forward in many videos such as TV programs and movies because a scene change usually coincides with a change in the camera's perspective. All you have to do is look for theses changes, a task that is made easier if the images are well lit and have relatively little blur, as is the case in most professional recorded films.

Life stream videos are different. Here the camera's perspective is always the same while the individual frames are often blurred by movement, washed out in scenes with too much light or blacked out in scenes with too little. All of this makes the task of scene change recognition that much harder.

Today, Svebor Karaman et amis at the University of Bordeaux in France say they have taken a step towards solving this problem with a new way of categorising daily activities in footage taken from a shoulder-mounted camera.

They define a scene as a sequence of frames in which the camera is relatively still, which they can easily determine by measuring trajectory of the corners of the image. They then categorise each sequence according to the colours present in the frames, which remain relatively constant even when the individual frames are blurred or dim. Finally, they manually label these scense with titles such as "moving in the kitchen" or "moving in home office" .

A computer can then use this information to detect similar patterns elsewhere in the footage. The result is a reasonably accurate picture of the activities that an ordinary person caries out on a daily basis.

See the full Story via external site: www.technologyreview.com

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