TY - GEN
T1 - Detecting Human Walking Direction Using Wi-Fi Signals
AU - Ali, Hanan Awad Hassan
AU - Seytnazarov, Shinnazar
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.
PY - 2024
Y1 - 2024
N2 - Using wireless signals, it is possible to map some patterns in the received signal to human activities in the vicinity of the wireless receiver. One of the potential applications of this is to detect the direction of human movement in a user device-free manner. To achieve it, we propose to use the channel state information (CSI) of received Wi-Fi signals and process the raw CSI using calibration, Hampel filter, and discrete wavelet transform to minimize the noise and retrieve the useful features of phase and amplitude components of the CSI. Then machine learning algorithms are applied to processed CSI to classify the direction of human walk. Our experimental study with off-the-shelf commodity Wi-Fi hardware and diverse users showed that the proposed system can produce 92.9%, 95.1%, and 89% accuracy for data from two different environments, combined data, respectively.
AB - Using wireless signals, it is possible to map some patterns in the received signal to human activities in the vicinity of the wireless receiver. One of the potential applications of this is to detect the direction of human movement in a user device-free manner. To achieve it, we propose to use the channel state information (CSI) of received Wi-Fi signals and process the raw CSI using calibration, Hampel filter, and discrete wavelet transform to minimize the noise and retrieve the useful features of phase and amplitude components of the CSI. Then machine learning algorithms are applied to processed CSI to classify the direction of human walk. Our experimental study with off-the-shelf commodity Wi-Fi hardware and diverse users showed that the proposed system can produce 92.9%, 95.1%, and 89% accuracy for data from two different environments, combined data, respectively.
KW - 802.11
KW - Channel state information
KW - Human activity recognition
KW - Machine learning
KW - Walking direction detection
KW - Wi-Fi
UR - http://www.scopus.com/inward/record.url?scp=85201007995&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85201007995&partnerID=8YFLogxK
U2 - 10.1007/978-981-97-2004-0_32
DO - 10.1007/978-981-97-2004-0_32
M3 - Conference contribution
AN - SCOPUS:85201007995
SN - 9789819720033
T3 - Lecture Notes in Networks and Systems
SP - 449
EP - 460
BT - Applied Soft Computing and Communication Networks - Proceedings of ACN 2023
A2 - Thampi, Sabu M.
A2 - Hu, Jiankun
A2 - Das, Ashok Kumar
A2 - Mathew, Jimson
A2 - Tripathi, Shikha
PB - Springer Science and Business Media Deutschland GmbH
T2 - International Conference on Applied Soft Computing and Communication Networks, ACN 2023
Y2 - 18 December 2023 through 20 December 2023
ER -