Translation resilient opportunistic WiFi sensing

TY - GEN

T1 - Translation resilient opportunistic WiFi sensing

AU - Bocus, Mohammud J.

AU - Li, Wenda

AU - Paulavicius, Jonas

AU - McConville, Ryan

AU - Santos-Rodriguez, Raul

AU - Chetty, Kevin

AU - Piechocki, Robert

N1 - Funding Information: This work was funded under the OPERA Project, the UK Engineering and Physical Sciences Research Council (EPSRC), Grant EP/R018677/1. Copyright: © 2021, IEEE.

PY - 2021/5/5

Y1 - 2021/5/5

N2 - Passive wireless sensing using WiFi signals has become a very active area of research over the past few years. Such techniques provide a cost-effective and non-intrusive solution for human activity sensing especially in healthcare applications. One of the main approaches used in wireless sensing is based on fine-grained WiFi Channel State Information (CSI) which can be extracted from commercial Network Interface Cards (NICs). In this paper, we present a new signal processing pipeline required for effective wireless sensing. An experiment involving five participants performing six different activities was carried out in an office space to evaluate the performance of activity recognition using WiFi CSI in different physical layouts. Experimental results show that the CSI system has the best detection performance when activities are performed half-way in between the transmitter and receiver in a line-of-sight (LoS) setting. In this case, an accuracy as high as 91% is achieved while the accuracy for the case where the transmitter and receiver are co-located is around 62%. As for the case when data from all layouts is combined, which better reflects the real-world scenario, the accuracy is around 67%. The results showed that the activity detection performance is dependent not only on the locations of the transmitter and receiver but also on the positioning of the person performing the activity.

AB - Passive wireless sensing using WiFi signals has become a very active area of research over the past few years. Such techniques provide a cost-effective and non-intrusive solution for human activity sensing especially in healthcare applications. One of the main approaches used in wireless sensing is based on fine-grained WiFi Channel State Information (CSI) which can be extracted from commercial Network Interface Cards (NICs). In this paper, we present a new signal processing pipeline required for effective wireless sensing. An experiment involving five participants performing six different activities was carried out in an office space to evaluate the performance of activity recognition using WiFi CSI in different physical layouts. Experimental results show that the CSI system has the best detection performance when activities are performed half-way in between the transmitter and receiver in a line-of-sight (LoS) setting. In this case, an accuracy as high as 91% is achieved while the accuracy for the case where the transmitter and receiver are co-located is around 62%. As for the case when data from all layouts is combined, which better reflects the real-world scenario, the accuracy is around 67%. The results showed that the activity detection performance is dependent not only on the locations of the transmitter and receiver but also on the positioning of the person performing the activity.

KW - Wireless communication

KW - Wireless sensor networks

KW - Transmitters

KW - Layout

KW - Pipelines

KW - Receivers

KW - Activity recognition

U2 - 10.1109/icpr48806.2021.9412263

DO - 10.1109/icpr48806.2021.9412263

M3 - Conference contribution

SN - 9781728188096

SP - 5627

EP - 5633

BT - Proceedings of ICPR 2020

PB - IEEE

CY - Milan

T2 - 25th International Conference on Pattern Recognition

Y2 - 10 January 2021 through 15 January 2021

ER -