期刊
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
卷 22, 期 6, 页码 1754-1764出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2018.2819182
关键词
Industry 4.0; human activity recognition; elderly healthcare; unobtrusive monitoring; CNN
类别
资金
- National Natural Science Foundation of China [61871437]
- National High Technology Research and Development Program [2015AA015901]
- JSPS KAKENHI [JP16K00117, JP15K15976]
- KDDI Foundation
- Human activity recognition (HAR) is widely applied to many industrial applications. In the context of Industry 4.0, driven by the same demand of machines' self-organizing ability, HAR can also be adopted in elderly healthcare. However, HAR should be adaptive to the application scenarios in elderly healthcare. In this paper, we propose a nonintrusive activity recognition method that can be applied to long-term and unobtrusive monitoring for elderlies. The method is robust to obstruction and nontarget object interference. Skeleton sequence is estimated from RGB images. Based on two activity continuity metrics, an interframe matching algorithm is proposed to filter nontarget objects. In order to make full use of spatial-temporal information, we propose a novel activity encoding method based on the interframe joints distances. A convolutional neural network is used to learn the distinguishing features automatically. A specific data augmentation method is designed to avoid the overfitting problem on small-scale datasets. The experiments are performed on two public activity datasets and a newly released noisy activity dataset (NAD). The NAD contains obstruction, nontarget object interference. The experimental results show that the proposed method achieves the state-of-the-art performance while only using one ordinary camera. The proposed method is robust to a realistic environment.
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