Automatic Extraction of Behavioral Patterns for Elderly Mobility and Daily Routine Analysis

Abstract

The elderly living in smart homes can have their daily movement recorded and analyzed. Given the fact that different elders can have their own living habits, a methodology that can automatically identify their daily activities and discover their daily routines will be useful for better elderly care and support. In this thesis research, we focus on developing data mining algorithms for automatic detection of behavioral patterns from the trajectory data of an individual for activity identification, daily routine discovery, and activity prediction.

A Review of the Recent Developments in Integrating Machine Learning Models with Sensor Devices in the Smart Buildings Sector with a View to Attaining Enhanced Sensing, Energy Efficiency, and Optimal Building Management

“…Examining the papers selected and summarized in Table S15, presented in the Supplementary Materials file, it can be observed that 67% of them take into consideration smart buildings in general, while the remaining 33% refer to smart homes. The authors of these scientific articles made use of different types of sensors in their analyses, including binary sensors [26]; sensor networks [140]; smart meters, Personal Weather Stations (PWS), and sensors providing data useful in computing the mean values of: hourly indoor temperature, hourly outdoor temperature, hourly value of precipitation, hourly value of wind direction, hourly value of solar radiation, hourly value of ultraviolet index, hourly value of humidity, hourly value of pressure [42]. In these papers, the reasons for using the K-Means method with the sensor devices in smart buildings were related to extraction of behavioral patterns [26]; determining electricity consumption patterns [140]; and managing energy consumption [42].…”

“…In these papers, the reasons for using the K-Means method with the sensor devices in smart buildings were related to extraction of behavioral patterns [26]; determining electricity consumption patterns [140]; and managing energy consumption [42]. With respect to the devised research methods, in [26], Li et al made use of a hybrid approach, combining the K-Means algorithm with Nominal Matrix Factorization method. In [140], Pérez-Chacón et al used the Cluster Validation Indices (CVIs) method to establish the optimal number of clusters for the dataset, combined with the parallelized version of K-Means clustering algorithm for discovering patterns from the dataset.…”

“…The performance metrics considered in the scientific papers using the K-Means integrated with sensor devices in smart buildings were evaluated based on a comparison with existing methods based on both synthetic and publicly available real smart home datasets [26]; cluster analysis, centroids of the electricity consumption clusters, centroids of the clusters with lower consumptions, and computing times [140]; and support, confidence and lift [42].…”

“…Regarding the most recent scientific articles that make use of the K-Means method along with sensor devices in smart buildings (Table 15), it can be observed that in [26], Li et al aimed to devise a methodology for the automatic detection of the behavioral patterns of elderly people living in smart homes. The authors made use of binary sensors and devised a hybrid approach, combining the K-Means algorithm with Nominal Matrix Factorization method in order to obtain the daily routines.…”

“…There is an increasing interest in the scientific literature in studies related to these topics; for example, there have been papers focusing on smart buildings [1][2][3][4][5], smart homes [6][7][8][9][10], smart hospitals [11], smart commercial buildings [12,13], sensor devices [9,[14][15][16][17], supervised machine learning models for classification purposes [1,11,[16][17][18] or for regression purposes [19][20][21][22][23], unsupervised machine learning models for clustering purposes [24][25][26], deep learning techniques [18,27,28], human activity recognition and classification with a view to assisted living [15,[29][30][31][32][33][34][35], Internet of Things (IoT) [21,[36][37][38]…”