Improving Human Activity Recognition in Smart Homes

Abstract

Even if it is now simple and cheap to collect sensors information in a smart home environment, the main issue remains to infer high-level activities from these simple readings. The main contribution of this work is twofold. Firstly, the authors demonstrate the efficiency of a new procedure for learning Optimized Cost-Sensitive Support Vector Machines (OCS-SVM) classifier based on the user inputs to appropriately tackle the problem of class imbalanced data. It uses a new criterion for the selection of the cost parameter attached to the training errors. Secondly, this method is assessed and compared with the Conditional Random Fields (CRF), Linear Discriminant Analysis (LDA), k-Nearest Neighbours (k-NN) and the traditional SVM. Several and various experimental results obtained on multiple real world human activity datasets using binary and ubiquitous sensors show that OCS-SVM outperforms the previous state-of-the-art classification approach.