絞り込み

16636

広告

Vision-based Freezing of Gait Detection with Anatomic Directed Graph Representation.

著者 Hu K , Wang Z , Mei S , Ehgoetz K , Yao T , Lewis S , Feng D この記事をPubMed上で見るPubMedで表示
この記事をGoogle翻訳上で見る Google翻訳で開く

スターを付ける スターを付ける     (3view , 0users)

Full Text Sources

Parkinson's disease significantly impacts the life quality of millions of people around the world. While freezing of gait (FoG) is one of the most common symptoms of the disease, it is time-consuming and subjective to assess FoG for well-trained experts. Therefore, it is highly desirable to devise computer-aided FoG detection methods for the purpose of objective and time-efficient assessment. In this study, in line with the gold standard of FoG clinical assessment which requires video or direct observation, we propose one of the first vision-based methods for automatic FoG detection. To better characterize FoG patterns, instead of learning an overall representation of a video, we propose a novel architecture of graph convolution neural network and represent each video as a directed graph where FoG related candidate regions are the vertices. A weakly-supervised learning strategy and a weighted adjacency matrix estimation layer are proposed to eliminate the resource expensive data annotation required for fully supervised learning. As a result, the interference of visual information irrelevant to FoG, such as gait motion of supporting staff involved in clinical assessments, has been reduced to improve FoG detection performance by identifying the vertices contributing to FoG events. To further improve the performance, the global context of a clinical video is also considered and several fusion strategies with graph predictions are investigated. Experimental results on more than 100 videos collected from 45 patients during a clinical assessment demonstrated promising performance of our proposed method with an AUC of 0.887.
PMID: 31217134 [PubMed - as supplied by publisher]
印刷用ページを開く Endnote用テキストダウンロード