TY - GEN
T1 - Identification of Hearing Impaired People in Crowded Environments Using Wi-Fi Signals
AU - Hameed, Hira
AU - Lubna,
AU - Usman, Muhammad
AU - Arshad, Kamran
AU - Hussain, Amir
AU - Assaleh, Khaled
AU - Imran, Muhammad Ali
AU - Abbasi, Qammer H.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - In this article, Wi-Fi signals are used to find hearing-impaired people in a crowded environment with the help of different gestures namely 'empty, one-hand right, one-hand left, two-hand right, and two-hand left'. The existing system for recognizing hearing-impaired people is based on cameras. This has some drawbacks, such as poor photo quality at night, privacy concerns, and the high cost of putting these systems into everyday life. The data collected from the Wi-Fi is represented in the form of channel state information (CSI) values. Five activities were performed by the subject namely empty, one-hand right, one-hand left, two-hand right, and two-hand left. Support vector machine (SVM) (Linear SVM), Ensemble (Subspace discriminant), and neural network pattern recognition were performed on collected CSI values. The simulation results showed that 94.7% of classification accuracy was achieved by neural network pattern recognition while classifying gesture data.
AB - In this article, Wi-Fi signals are used to find hearing-impaired people in a crowded environment with the help of different gestures namely 'empty, one-hand right, one-hand left, two-hand right, and two-hand left'. The existing system for recognizing hearing-impaired people is based on cameras. This has some drawbacks, such as poor photo quality at night, privacy concerns, and the high cost of putting these systems into everyday life. The data collected from the Wi-Fi is represented in the form of channel state information (CSI) values. Five activities were performed by the subject namely empty, one-hand right, one-hand left, two-hand right, and two-hand left. Support vector machine (SVM) (Linear SVM), Ensemble (Subspace discriminant), and neural network pattern recognition were performed on collected CSI values. The simulation results showed that 94.7% of classification accuracy was achieved by neural network pattern recognition while classifying gesture data.
KW - Human gesture
KW - Machine learning
KW - RF sensing
KW - Wi-Fi
UR - https://www.scopus.com/pages/publications/85172415815
U2 - 10.1109/USNC-URSI52151.2023.10237903
DO - 10.1109/USNC-URSI52151.2023.10237903
M3 - Conference contribution
AN - SCOPUS:85172415815
T3 - IEEE Antennas and Propagation Society, AP-S International Symposium (Digest)
SP - 279
EP - 280
BT - 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 IEEE International Symposium on Antennas and Propagation and USNC-URSI Radio Science Meeting, AP-S/URSI 2023
Y2 - 23 July 2023 through 28 July 2023
ER -