Jung, Dawoon Mau Dung Nguyen Arshad, Muhammad Zeeshan Kim, Jinwook Mun, Kyung-Ryoul 2024-01-12T03:44:04Z 2024-01-12T03:44:04Z 2022-04-30 2021-11 1557-170X https://pubs.kist.re.kr/handle/201004/77300 Human gait can serve as a useful behavioral trait for biometrics. Compared to fingerprint, face, and iris, the most commonly used physiological identifiers, gait can be unobtrusively monitored from a distance without requiring explicit involvement and physical restraint from people. Advances in wearable technology facilitate direct and faithful measurement of gait motions with easy-to-use and low-cost inertial sensors. This study aimed to propose an approach to using kinematic gait data collected with wearable inertial sensors for reliable personal identification. Sixty-nine individuals ranged in age from 24 to 62 years old participated in this study. The 3-axis acceleration and the 3-axis angular velocity signals were measured using the inertial measurement units attached to the feet, shanks, thighs, and posterior pelvis while walking. The gait spectrograms were acquired by applying time-frequency analyses to the lower body movement signals measured in one stride. Among each participant's 15 strides, 12 strides were used in the 4-fold cross validation of the deep convolutional neural network-based classifiers, and the remaining three strides were used to evaluate the classifiers. An accuracy of 99.69% was achieved by using the foot, shank, thigh, and pelvic spectrograms, and the accuracy was 96.89% using only the foot spectrograms. This study has the potential to be applied in behavior-based biometric technologies by confirming the feasibility of the proposed kinematic and spectrographic approaches in identifying personal behavioral characteristics. English IEEE Personal Identification Using Gait Spectrograms and Deep Convolutional Neural Networks Conference 10.1109/EMBC46164.2021.9630315 1 43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC), pp.6899 - 6904 43rd Annual International Conference of the IEEE-Engineering-in-Medicine-and-Biology-Society (IEEE EMBC) 6899 6904 US ELECTR NETWORK 2021-10-31 2021 43RD ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE & BIOLOGY SOCIETY (EMBC) 000760910506119 2-s2.0-85122492234