TY - GEN
T1 - Demo
T2 - 2019 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2019
AU - Shi, Cong
AU - Esposito, Justin
AU - Mathew, Sachin
AU - Patel, Amit
AU - Sakhuja, Rishika
AU - Liu, Jian
AU - Chen, Yingying
N1 - Publisher Copyright:
© 2019 IEEE.
PY - 2019/11
Y1 - 2019/11
N2 - In this demo, we present a device-free activity monitoring platform exploiting the prevalent WiFi signals to enable real-time activity recognition and user identification in indoor environments. It supports a broad array of real-world applications, such as senior assistance services, fitness tracking, and building surveillance. In particular, the proposed platform takes advantage of channel state information (CSI), which is sensitive to environmental changes introduced by human body movements. To enable immediate response of the platform, we design a real-time mechanism that continuously monitors the WiFi signals and promptly analyzes the CSI readings when the human activity is detected. For each detected activity, we extract representative features from CSI, and exploit a deep neural network (DNN) based scheme to accurately identify the activity type/user identity. Our experimental results demonstrate that the proposed platform could perform activity/user identification with high accuracy while offering low latency.
AB - In this demo, we present a device-free activity monitoring platform exploiting the prevalent WiFi signals to enable real-time activity recognition and user identification in indoor environments. It supports a broad array of real-world applications, such as senior assistance services, fitness tracking, and building surveillance. In particular, the proposed platform takes advantage of channel state information (CSI), which is sensitive to environmental changes introduced by human body movements. To enable immediate response of the platform, we design a real-time mechanism that continuously monitors the WiFi signals and promptly analyzes the CSI readings when the human activity is detected. For each detected activity, we extract representative features from CSI, and exploit a deep neural network (DNN) based scheme to accurately identify the activity type/user identity. Our experimental results demonstrate that the proposed platform could perform activity/user identification with high accuracy while offering low latency.
KW - Activity Monitoring
KW - Channel State Information
KW - WiFi
UR - http://www.scopus.com/inward/record.url?scp=85077979199&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85077979199&partnerID=8YFLogxK
U2 - 10.1109/DySPAN.2019.8935843
DO - 10.1109/DySPAN.2019.8935843
M3 - Conference contribution
AN - SCOPUS:85077979199
T3 - 2019 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2019
BT - 2019 IEEE International Symposium on Dynamic Spectrum Access Networks, DySPAN 2019
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 11 November 2019 through 14 November 2019
ER -