A Novel Approach Based on Time Cluster for Activity Recognition of Daily Living in Smart Homes

被引:15
作者
Liu, Yaqing [1 ,2 ,3 ,4 ]
Ouyang, Dantong [2 ]
Liu, Yong [3 ]
Chen, Rong [1 ]
机构
[1] Dalian Maritime Univ, Sch Informat Sci & Technol, Dalian 116026, Peoples R China
[2] Jilin Univ, Key Lab Symbol Computat & Knowledge Engn, Minist Educ, Changchun 130012, Jilin, Peoples R China
[3] Sichuan Univ Sci & Engn, Artificial Intelligence Key Lab Sichuan Prov, Zigong 643000, Peoples R China
[4] Nanjing Univ Informat Sci & Technol, Sch Comp & Software, Nanjing 210044, Jiangsu, Peoples R China
来源
SYMMETRY-BASEL | 2017年 / 9卷 / 10期
基金
中国国家自然科学基金;
关键词
smart homes; activity recognition; sensors; ENVIRONMENTS; ALGORITHM; CLASSIFICATION; SEGMENTATION; REGRESSION; AMBIENT; STATE;
D O I
10.3390/sym9100212
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
With the trend of the increasing ageing population, more elderly people often encounter some problems in their daily lives. To enable these people to have more carefree lives, smart homes are designed to assist elderly people by recognizing their daily activities. Although different models and algorithms that use temporal and spatial features for activity recognition have been proposed, the rigid representations of these features damage the accuracy of activity recognition. In this paper, a two-stage approach is proposed to recognize the activities of a single resident. Firstly, in terms of temporal features, the approximate duration, start and end time are extracted from the activity records. Secondly, a set of activity records is clustered according to the records' temporal features. Then, the classifiers are used to recognize the daily activities in each cluster according to the spatial features. Finally, two experiments are done to validate the recognition of daily activities in order to compare the proposed approach with a one-dimensional model. The results demonstrate that the proposed approach favorably outperforms the one-dimensional model. Two public datasets are used to evaluate the proposed approach. The experiment results show that the proposed approach achieves average accuracies of 80% and 89%, respectively.
引用
收藏
页数:17
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