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Your search keyword '"Saho, Kenshi"' showing total 118 results
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118 results on '"Saho, Kenshi"'

11. Micro‐Doppler Radar‐Based Gait Classification of Common Pedestrians and Smartphone Zombies.

Author

Yasuda, Kazuki, Tsuyuhara, Teppei, Saho, Kenshi, and Masugi, Masao

Subjects
DOPPLER radar, CONVOLUTIONAL neural networks, GAIT in humans, SMARTPHONES, PEDESTRIANS
Abstract

This paper proposes a micro‐Doppler radar‐based method to classify common pedestrians and pedestrians texting on a smartphone (called a smartphone zombies). We used a micro‐Doppler radar to collect motion data of the gait of participants texting on smartphones while walking, and then generated time‐frequency distribution images (spectrogram images). They were input into a convolutional neural network (CNN) to classify gait patterns. Results using measured data confirmed that the classification accuracy exceeded 90%, validating the effectiveness of the proposed method. Furthermore, by applying feature visualization by Grad‐Cam, we found that the motion of the leg swinging has essential features for the classification of smartphone zombies. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Published
2023
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34. Kalman Filter For Moving Object Tracking : Performance Analysis and Filter Design

Author

Saho, Kenshi

Subjects
Mathematics / Applied
Abstract

This chapter presents Kalman filters for tracking moving objects and their efficient design strategy based on steady-state performance analysis. First, a dynamic/measurement model is defined for the tracking systems, assuming both position-only and position-velocity measurements. Then, problems with the Kalman filter design in tracking systems are summarized, and an efficient steady-state performance index proposed by the author [termed the root-mean-squared error index (the RMS index)] is introduced to resolve these concerns. The analytical relationship between the proposed RMS index and the covariance matrix of the process noise is shown, leading to a proposed design strategy that is based on this relationship. Theoretical performance analysis is conducted using the performance indices to show the optimality of the design strategy. Numerical simulations show the validity of the theoretical analyses and effectiveness of the proposed strategy in realistic situations. In addition, the optimal performance of the position-only-measured and position-velocity-measured systems is analyzed and compared. This comparison shows that the position-velocity-measured Kalman filter tracking is accurate when compared with the position-only-measured filter.

Published
2018

44. Personal Identification Based on the Picking‐Up Movement Measured Using a Doppler Radar.

Author

Saho, Kenshi and Shioiri, Keitaro

Subjects
*DOPPLER radar, *CONVOLUTIONAL neural networks, *DEEP learning, *ELECTRICAL engineers
Abstract

This study demonstrates personal identification based on the movement of picking up an object placed on the floor. A Doppler radar identifies the picking‐up movement, consequently, the radar images of time–velocity distribution (spectrogram images) were input to a convolutional neural network to recognize the individuals. The experimental results showed an 85.9% accuracy in identifying six men. This suggests that the picking‐up movement can be used as a novel biometric method. © 2021 Institute of Electrical Engineers of Japan. Published by Wiley Periodicals LLC. [ABSTRACT FROM AUTHOR]

Published
2021
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