TY - GEN
T1 - No One Left Behind
T2 - 2020 IEEE International Conference on Communications, ICC 2020
AU - Shi, Dian
AU - Lu, Jixiang
AU - Wang, Jie
AU - Li, Lixin
AU - Liu, Kaikai
AU - Pan, Miao
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - According to the safety organization Kids and Cars, in US, an average of 38 children die each year in hot cars, seemingly forgotten by a distracted parent. Existing car seat alarm designs either compromise people's privacy (camera based designs), or fail to distinguish children sitting in the back from heavy stuff put on rear seats, and keep sending false alerts (pressure sensor based designs). In an effort to prevent such tragedies, we propose to utilize the fine-grained channel state information (CSI) from commercial off-the-shelf WiFi devices to detect if a child has been forgotten in rear seat of the car. Our child detection system only needs WiFi signal and applies both phase and amplitude measurement of the CSI. Based on this, our system can capture the movements of children, and effectively detect the children who are forgotten in rear seat and distinguish them from pets or other heavy stuff in rear seat with deep learning algorithms. In comparison with KNN based child detection method, the experiment results show that the performance of our deep learning based system increases dramatically, and the detection accuracy can reach more than 95%.
AB - According to the safety organization Kids and Cars, in US, an average of 38 children die each year in hot cars, seemingly forgotten by a distracted parent. Existing car seat alarm designs either compromise people's privacy (camera based designs), or fail to distinguish children sitting in the back from heavy stuff put on rear seats, and keep sending false alerts (pressure sensor based designs). In an effort to prevent such tragedies, we propose to utilize the fine-grained channel state information (CSI) from commercial off-the-shelf WiFi devices to detect if a child has been forgotten in rear seat of the car. Our child detection system only needs WiFi signal and applies both phase and amplitude measurement of the CSI. Based on this, our system can capture the movements of children, and effectively detect the children who are forgotten in rear seat and distinguish them from pets or other heavy stuff in rear seat with deep learning algorithms. In comparison with KNN based child detection method, the experiment results show that the performance of our deep learning based system increases dramatically, and the detection accuracy can reach more than 95%.
UR - http://www.scopus.com/inward/record.url?scp=85089426520&partnerID=8YFLogxK
U2 - 10.1109/ICC40277.2020.9148648
DO - 10.1109/ICC40277.2020.9148648
M3 - 会议稿件
AN - SCOPUS:85089426520
T3 - IEEE International Conference on Communications
BT - 2020 IEEE International Conference on Communications, ICC 2020 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 7 June 2020 through 11 June 2020
ER -