Activity Recognition in Sensor Data Streams for Active and Assisted Living Environments

Abstract

In active and assisted living environments, a major service that can be provided is the automated assessment of elderly people's well-being. Therefore, activity recognition is required to detect what types of help disabled persons need to support them in their daily life activities. Unfortunately, it is still a difficult task to estimate the size of the required window for online sensor data streams to recognize a specific activity, especially when new sensor events are recorded. This paper proposes a windowing algorithm, which presents promising results to recognize complex human activities for multi-resident homes. The approach is based on the analysis of the sensor data to identify the best fitting sensors that should be considered in a specified window. Moreover, the second part of this paper proposes a set of different statistical spatio-temporal features to recognize human activities. In order to check the overall performance, this approach is tested using the CASAS data set and artificially generated laboratory data using our HBMS simulator. The results show high performance based on different evaluation metrics compared to other approaches. We believe that the proposed windowing approach provides a good approximation of the required window size in order to robustly detect human activities in comparison to other windowing approaches.

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

“…In their research, the authors of these papers implement various types of sensors, according to their purposes, namely: indoor sensors [1], occupancy information sensors [1], electricity meters [1,6,44], motion sensors [6,7,30,59,60], item kitchen sensors [6], door sensors [6,59,61,62], temperature sensors [1,2,6,59,63], photosensors [1,3,63], status of water and burner sensors [6,59], acceleration sensors [4,7], Kinect motion sensors [7], modern smartphone sensors [4,7,60], passive radar-based sensors [8], unobtrusive sensors [9,14], infrared sensors [15,30], wireless sensor networks [61,62], accelerometers [5,63], altimeters [63], gyroscopes [63], barometers [63], heart rate monitor [63], embedded sensors [4,…”

“…With respect to the reasons for using the SVM method with sensor equipment in smart buildings, it can be observed that the recognition of human activity is at the forefront, as this is addressed in most of the papers [3,4,6,[8][9][10]14,15,[29][30][31][32]59,60,62,63]. Assisted living was a strong motivation for using the SVM method with sensor devices in the smart buildings sector; seven of the identified papers focusing on the recognition of human activity did so in order to provide appropriate assisted living [6,14,15,[30][31][32]63], while other papers aimed to achieve assisted living by focusing on human fall detection [7], human behavior recognition [2], assessment of occupancy status information, and identification of human behavior [61]. Other reasons for applying SVM with sensors in smart buildings include measuring the occupancy status of a building's inhabitants in order to improve the energy prediction performance of the building's energy model [1], classifying the gender of occupants [5], forecasting electricity consumption [44], detecting and classifying human behavior with a view to maximizing comfort with optimized energy consumption [52], recognizing household appliances in order to assess their usage and develop habits of power preservation [64], and selecting optimal sensors for use in complex system monitoring problems such as HVAC chillers [65].…”

“…With respect to the devised methods, in [1], the authors made use of the Support Vector Machine technique and compared the obtained results with those obtained using Decision Tree and Artificial Neural Networks. In [6], the Support Vector Machine approach was implemented with a polynomial kernel of degree 3 (P-SVM), and afterwards, a comparison was conducted with other four classifiers: Radial Basis Function kernel-Support Vector Machine (RBF-SVM), Naïve Bayes, logistic recognition, and Recurrent Neural Network (RNN). The authors of [7,8,32,52,59,60] developed their research based solely on the Support Vector Machine technique.…”

“…The performance metrics considered in the scientific papers that used Support Vector Machines integrated with sensor devices in smart buildings include: Accuracy [1,3,5,[7][8][9][10]29,31,59,61,62,64]; Standard Deviation [1,63]; True Positive Rate [6,7,59]; False Positive Rate [6,7,59]; Precision [6,29,30,59,61,62]; Recall [6,29,59,61,62]; F-measure [6,29,30,59,61,62]; True Negative rate [7,59]; False Negative Rate [7,59]; Sensitivity [7,14,30]; Specificity [7,14,30,59];...…”

“…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]…”