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. 2020 Mar 6;20(5):1457.
doi: 10.3390/s20051457.

Recognition of Daily Activities of Two Residents in a Smart Home Based on Time Clustering

Jinghuan Guo  1   2 , Yiming Li  1 , Mengnan Hou  1 , Shuo Han  1 , Jianxun Ren  1
Affiliations

Recognition of Daily Activities of Two Residents in a Smart Home Based on Time Clustering

Jinghuan Guo et al. Sensors (Basel). .

Abstract

With the development of population aging, the recognition of elderly activity in smart homes has received increasing attention. In recent years, single-resident activity recognition based on smart homes has made great progress. However, few researchers have focused on multi-resident activity recognition. In this paper, we propose a method to recognize two-resident activities based on time clustering. First, to use a de-noising method to extract the feature of the dataset. Second, to cluster the dataset based on the begin time and end time. Finally, to complete activity recognition using a similarity matching method. To test the performance of the method, we used two two-resident datasets provided by Center for Advanced Studies in Adaptive Systems (CASAS). We evaluated our method by comparing it with some common classifiers. The results show that our method has certain improvements in the accuracy, recall, precision, and F-Measure. At the end of the paper, we explain the parameter selection and summarize our method.

Keywords: activity recognition; sensor; smart home.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Figure 1
Figure 1
Process for the activity recognition.
Figure 2
Figure 2
Tulum2010 sensor layout.
Figure 3
Figure 3
Cairo sensor layout.
Figure 4
Figure 4
Relationship between k and sum of the squared errors (SSE) in Tulum2010.
Figure 5
Figure 5
Relationship between n and accuracy when weight of ratio (w1) = 0.15 and w2 = 0.7.
See this image and copyright information in PMC

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