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

Abstract

Abstract: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.

Activity Feature Solving Based on TF‐IDF for Activity Recognition in Smart Homes

“…Activity recognition in smart homes can be divided into knowledge-driven and data-driven [13][14][15]. For knowledgedriven approach, knowledge is generated from field experts.…”

Introduction to the Special Issue: Applications of Internet of Things

“…Two papers on smart home are included as follows: (1) "A novel approach based on time clusters for activity recognition of daily living in smart homes", by Liu et al [48]; and (2) "IoT-based image recognition systems for smart home-delivered meal services", by Tseng et al [49].…”

“…In Stage 1, the practical sensing data can be clustered in accordance with temporal features; in Stage 2, some classification methods (e.g., naive Bayesian classifier, k-nearest neighbors, decision tree, and random forest) were applied to analyze the practical sensing data of each cluster, and to a generate classification model for each cluster. In experimental environments, the two open datasets from the "single-resident apartment data" provided by Washington State University were collected to evaluate the proposed method, and the results showed that the average classification accuracy of the proposed two-stage time clustering method is higher than the one-stage method without time clustering [48].…”

A hybrid approach of knowledge-driven and data-driven reasoning for activity recognition in smart homes

“…Activity recognition is considered an important area of research, particularly in the field of healthcare services [1]. The significance of this area is mainly due to the provision of support and assistance for elderly, disabled and cognitively impaired people [2]. Furthermore, activity recognition has become a primary indicator to measure physical and mental health of elderly individuals based on their ability to perform basic activities such as bathing, eating and cooking [3].…”