A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Challenges: device-free passive localization for wireless environments
Proceedings of the 13th annual ACM international conference on Mobile computing and networking
Radio Tomographic Imaging with Wireless Networks
IEEE Transactions on Mobile Computing
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Passive detection of situations from ambient FM-radio signals
Proceedings of the 2012 ACM Conference on Ubiquitous Computing
CoSDEO 2013: device-free radio-based recognition
Proceedings of the 2013 ACM conference on Pervasive and ubiquitous computing adjunct publication
Proceedings of International Conference on Advances in Mobile Computing & Multimedia
| Hi-index | 0.00 |
In this paper we introduce a novel device-free radio based activity recognition with localization method with various applications, such as e-Healthcare and security. Our method uses the properties of the signal subspace, which are estimated using signal eigenvectors of the covariance matrix obtained from an antenna array (array sensor) at the receiver side. To classify human activities (e.g., standing and moving) and/or positions, we apply a machine learning method with support vector machines (SVM). We compare the classification accuracy of the proposed method with signal subspace features and received signal strength (RSS). We analyze the impact of antenna deployment on classification accuracy in non-line-of-sight (NLOS) environments to prove the effectiveness of the proposed method. In addition, we compare our classification method with k-Nearest Neighbor (KNN). The experimental results show that the proposed method with signal subspace features provides accuracy improvements over the RSS-based method.