日常生活活动指数的分层HMM在痴呆诊断中的应用
Activities of Daily Living Indexing by Hierarchical HMM for Dementia Diagnostics
摘要：
本文提出了一种方法，用于索引从患者佩戴的可穿戴相机捕获的视频中的人类活动，用于研究痴呆疾病的进展。我们的方法旨在产生索引，以促进在整个个人视频记录中的导航，这可以帮助医生在日常生活活动中寻找疾病的早期迹象。所记录的视频具有强烈的运动和急剧的照明变化，从而引起用于分析的噪声。所提出的方法是基于两个步骤的分析。首先，我们提出了一种新的方法来分割这种类型的视频，基于明显的运动。每个段的特点是由两个原始运动描述符，以及颜色和音频描述符。其次，隐马尔可夫模型公式被用来合并多模态音频和视频特征，并分类测试段。实验表明，该方法在实际数据上具有良好的性能.
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
