Two serious problems affecting the implementation of Human Activity Recognition (HAR) algorithms have been acknowledged. The first one corresponds to non-informative sequence features. The second is the class imbalance in the training data due to the fact that people do not spend the same amount of time on the different activities. To address these issues, we propose a new scheme based on a combination of Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and the modified Weighted Support Vector Machines (mWSVM). First we added the most significant principal components to the set of features extracted using LDA. This work shown that a suitable sequence feature set combined with the modified WSVM based our criterion classifier achieve good improvement and efficiency over the traditional used methods.
Different approaches based on various wireless technologies have been proposed so far for indoor localization. Radio frequency Identification (RFID) indoor localization seems to be a promising way of research. The identification capability of this technology combined to localization methods improves the results obtained by other wireless technologies such as Wifi, GPS, Zigbee... This paper details some localization techniques used for RFID Tags localization in Indoor environment. In particular, Fingerprinting methods are compared to Distance estimation methods. We will show through several simulation experiments, using NS2 and Matlab software, that fingerprinting techniques outperform Distance estimation techniques for localization and tracking tasks.
Purpose
– The task of identifying activity classes from sensor information in smart home is very challenging because of the imbalanced nature of such data set where some activities occur more frequently than others. Typically probabilistic models such as Hidden Markov Model (HMM) and Conditional Random Fields (CRF) are known as commonly employed for such purpose. The paper aims to discuss these issues.
Design/methodology/approach
– In this work, the authors propose a robust strategy combining the Synthetic Minority Over-sampling Technique (SMOTE) with Cost Sensitive Support Vector Machines (CS-SVM) with an adaptive tuning of cost parameter in order to handle imbalanced data problem.
Findings
– The results have demonstrated the usefulness of the approach through comparison with state of art of approaches including HMM, CRF, the traditional C-Support vector machines (C-SVM) and the Cost-Sensitive-SVM (CS-SVM) for classifying the activities using binary and ubiquitous sensors.
Originality/value
– Performance metrics in the experiment/simulation include Accuracy, Precision/Recall and F measure.
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.
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