期刊
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING
卷 26, 期 11, 页码 2189-2199出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSRE.2018.2875738
关键词
Timed up-and-go; Parkinson's disease; human pose estimation; sub-task segmentation
资金
- National Natural Science Foundation of China [61673234, 81527901]
- National Key Research and Development Program of China [2016YFC0105904, 2016YFC0105502]
The timed up-and-go (TUG) test has been widely accepted as a standard assessment for measuring the basic functional mobility of patients with Parkinson's disease. Several basic mobility sub-tasks Sit, Sit-to-Stand, Walk, Turn, Walk-Back, and Sit-Back are included in a TUG test. It has been shown that the time costs of these sub-tasks are useful clinical parameters for the assessment of Parkinson's disease. Several automatic methods have been proposed to segment and time these sub-tasks in a TUG test. However, these methods usually require either well-controlled environments for the TUG video recording or information from special devices, such as wearable inertial sensors, ambient sensors, or depth cameras. In this paper, an automatic TUG sub-task segmentation method using video-based activity classification is proposed and validated in a study with 24 Parkinson's disease patients. Videos used in this paper are recorded in semi-controlled environments with various backgrounds. The state-of-the-art deep learning-base 2-D human pose estimation technologies are used for feature extraction. A support vector machine and a long short-term memory network are then used for the activity classification and the subtask segmentation. Our method can be used to automatically acquire clinical parameters for the assessment of Parkinson's disease using TUG videos-only, leading to the possibility of remote monitoring of the patients' condition.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据