TY - GEN
T1 - WiDMove
T2 - 2018 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2018
AU - Soaresda Silva, Bruno
AU - Teodorolaureano, Gustavo
AU - Abdallah, Abdallah S.
AU - Vieiracardoso, Kleber
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/27
Y1 - 2018/8/27
N2 - The accurate detection of people in indoor environments requires high-cost devices, while low-cost devices, in addition to low accuracy, offer little information about the monitored events. The perturbations that result from indoor movements affect the signals received by 802.11 interfaces. Hence, an 802.11 device becomes a widely available, low-cost, and reasonably accurate solution for several applications. This paper presents WiDMove, a proposed technique to detect the entry and exit of persons, within an indoor environment, using the channel state information (CSI) measurements, which is provided by the IEEE 802.11n compliant devices. Based on the gathered CSI measurements, we utilized frequency-time analysis methodology to build an efficient features vector based on Short-Time Fourier Transform (STFT) and Principal Component Analysis (PCA). We used the extracted features to train and develop a Support Vector Machine (SVM) classifier, which provided very promising initial results. Our initial results have an accuracy near 80 %.
AB - The accurate detection of people in indoor environments requires high-cost devices, while low-cost devices, in addition to low accuracy, offer little information about the monitored events. The perturbations that result from indoor movements affect the signals received by 802.11 interfaces. Hence, an 802.11 device becomes a widely available, low-cost, and reasonably accurate solution for several applications. This paper presents WiDMove, a proposed technique to detect the entry and exit of persons, within an indoor environment, using the channel state information (CSI) measurements, which is provided by the IEEE 802.11n compliant devices. Based on the gathered CSI measurements, we utilized frequency-time analysis methodology to build an efficient features vector based on Short-Time Fourier Transform (STFT) and Principal Component Analysis (PCA). We used the extracted features to train and develop a Support Vector Machine (SVM) classifier, which provided very promising initial results. Our initial results have an accuracy near 80 %.
UR - http://www.scopus.com/inward/record.url?scp=85053632277&partnerID=8YFLogxK
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U2 - 10.1109/CCECE.2018.8447627
DO - 10.1109/CCECE.2018.8447627
M3 - Conference contribution
AN - SCOPUS:85053632277
SN - 9781538624104
T3 - Canadian Conference on Electrical and Computer Engineering
BT - 2018 IEEE Canadian Conference on Electrical and Computer Engineering, CCECE 2018
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 13 May 2018 through 16 May 2018
ER -