Activities of Daily Living Indexing by Hierarchical HMM for Dementia Diagnostics

Abstract : This paper presents a method for indexing human ac- tivities in videos captured from a wearable camera being worn by patients, for studies of progression of the dementia diseases. Our method aims to produce indexes to facilitate the navigation throughout the individual video recordings, which could help doctors search for early signs of the dis- ease in the activities of daily living. The recorded videos have strong motion and sharp lighting changes, inducing noise for the analysis. The proposed approach is based on a two steps analysis. First, we propose a new approach to segment this type of video, based on apparent motion. Each segment is characterized by two original motion de- scriptors, as well as color, and audio descriptors. Second, a Hidden-Markov Model formulation is used to merge the multimodal audio and video features, and classify the test segments. Experiments show the good properties of the ap- proach on real data.
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Submitted on : Wednesday, June 22, 2011 - 11:27:22 AM
Last modification on : Thursday, October 24, 2019 - 2:44:06 PM
Long-term archiving on : Friday, November 9, 2012 - 4:50:39 PM

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  • HAL Id : hal-00602203, version 1
  • ARXIV : 1106.4451

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Svebor Karaman, Jenny Benois-Pineau, Jean-François Dartigues, Yann Gaëstel, Rémi Mégret, et al.. Activities of Daily Living Indexing by Hierarchical HMM for Dementia Diagnostics. 2011 9th International Workshop on Content-Based Multimedia Indexing (CBMI), Jun 2011, Madrid, Spain. pp.79-84. ⟨hal-00602203⟩

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