TY - GEN
T1 - Ambient intelligence sensing using array sensor
T2 - 2013 ACM Conference on Ubiquitous Computing, UbiComp 2013
AU - Hong, Jihoon
AU - Ohtsuki, Tomoaki
N1 - Copyright:
Copyright 2013 Elsevier B.V., All rights reserved.
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Activity recognition
KW - Antenna array
KW - Device-free sensing
KW - Localization
UR - http://www.scopus.com/inward/record.url?scp=84885235690&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84885235690&partnerID=8YFLogxK
U2 - 10.1145/2494091.2497609
DO - 10.1145/2494091.2497609
M3 - Conference contribution
AN - SCOPUS:84885235690
SN - 9781450322157
T3 - UbiComp 2013 Adjunct - Adjunct Publication of the 2013 ACM Conference on Ubiquitous Computing
SP - 509
EP - 520
BT - UbiComp 2013 Adjunct - Adjunct Publication of the 2013 ACM Conference on Ubiquitous Computing
Y2 - 8 September 2013 through 12 September 2013
ER -