Activity recognition in smart homes with self verification of assignments

Abstract

This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent repository link AbstractActivity recognition in smart homes provides valuable benefits in the field of health and elderly care by remote monitoring of patients. In health care, capabilities of both performing the correct recognition and reducing the wrong assignments are of high importance. The novelty of the proposed activity recognition approach lies in being able to assign a category to the incoming activity, while measuring the confidence score of the assigned category that reduces the false positives in the assignments. Multiple sensors deployed at different locations of a smart home are used for activity observations. For multi-class activity classification, we propose a binary solution using support vector machines, which simplifies the problem to correct/incorrect assignments. We obtain the confidence score of each assignment by estimating the activity distribution within each class such that the assignments with low confidence are separated for further investigation by a human operator. The proposed approach is evaluated using a comprehensive performance evaluation metrics. Experimental results obtained from nine publicly available smart home datasets demonstrate a better performance of the proposed approach compared to the state of the art.

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].…”

“…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. In [2], the Support Vector Regression (SVR) and Recurrent Neural Network (RNN) approaches were used.…”

“…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];...…”

Human Activity Recognition in Smart Homes Based on a Difference of Convex Programming Problem

“…The HAR methods discussed above are instances of such works. Among data-driven HAR schemes, the approach of [34] manages the uncertainty of observations at the decision level. This is achieved through the verification of assigned labels, while utilizing the environment interactive sensors for the recognition of general activities.…”