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This paper presents a method for indexing activities of daily living invideos obtained from wearable cameras. In the context of dementia diagnosis bydoctors, the videos are recorded at patients' houses and later visualized bythe medical practitioners. The videos may last up to two hours, therefore atool for an efficient navigation in terms of activities of interest is crucialfor the doctors. The specific recording mode provides video data which arereally difficult, being a single sequence shot where strong motion and sharplighting changes often appear. Our work introduces an automatic motion basedsegmentation of the video and a video structuring approach in terms ofactivities by a hierarchical two-level Hidden Markov Model. We define ourdescription space over motion and visual characteristics of video and audiochannels. Experiments on real data obtained from the recording at home ofseveral patients show the difficulty of the task and the promising results ofour approach.

Hierarchical Hidden Markov Model in Detecting Activities of Daily Living in Wearable Videos for Studies of Dementia