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.
Highlights
The problem of aging in the world’s population is becoming increasingly serious; meaning, the proportion of the aging population is increasing while the fertility rate continues to decrease
We first compare our results with several common single classifiers, such as K-Nearest Neighbor (KNN), lib Support Vector Machine, Sequential Minimal Optimization (SMO), Naïve Bayes (NB), PIPPER, C4.5 and Random Forests (RF)
Noise reduction processing is performed on the features
Summary
The problem of aging in the world’s population is becoming increasingly serious; meaning, the proportion of the aging population is increasing while the fertility rate continues to decrease. Some empty-nest seniors are old and frail, and there is no one to take care of them in emergencies, such as falls, which may cause irreparable losses. To improve this problem, some researchers have begun to focus on elderly activity recognition in smart homes. Activity recognition is an important part of the functioning of smart homes, mainly as it could determine abnormalities in the elderly by obtaining information about their daily activities. This can help predict some potential diseases in the elderly, such as Alzheimer’s disease, which is the main motivation for many activity recognition studies in intelligent environments (and has widely been recognized by families affected by Alzheimer’s disease) [1]
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