arXiv subjects

Takuya Sakamoto

Publications and source records attributed to Takuya Sakamoto.

25 records · Page 2Linked to original sources

arXiv2023

Radar-Based Respiratory Measurement of a Rhesus Monkey by Suppressing Nonperiodic Body Motion Components

We propose a method to measure the respiration of a rhesus monkey using a millimeter-wave radar system with an antenna array. Unlike humans, small animals are generally restless and hyperactive in nature, and suppression of their body motion components is thus necessary to realize accurate respiratory measurements. The proposed method detects and suppresses nonperiodic body motion components while also combining and emphasizing the periodic components from multiple echoes acquired from the target. Results indicate that the proposed method can measure respiration rate of the target monkey accurately, even with frequent body movements.

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arXiv2021

Stepped-frequency DORT Imaging with Singular Value Decomposition for a Single-antenna Ultrawideband Radar in Multi-path Environments

Ultrawideband radar is an attractive technology for a variety of applications including security systems. As such, it is essential to develop low-cost systems that produce clear target images. Electromagnetic inverse scattering with time-reversal imaging has been studied for a variety of applications. The time-reversal method, however, uses large-scale antenna arrays, making the system potentially more costly. In this study, we propose an ultrawideband radar imaging algorithm, namely the stepped-frequency DORT algorithm, that uses multi-path scattering for a single antenna. The proposed imaging method is an extension of the conventional DORT method, and uses a frequency-frequency matrix that is suitable for a system with a single antenna. The performance of the proposed method is verified through numerical simulations.

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arXiv2021

Noncontact Respiratory Measurement for Multiple People at Arbitrary Locations Using Array Radar and Respiratory-Space Clustering

We developed a noncontact measurement system for monitoring the respiration of multiple people using millimeter-wave array radar. To separate the radar echoes of multiple people, conventional techniques cluster the radar echoes in the time, frequency, or spatial domain. Focusing on the measurement of the respiratory signals of multiple people, we propose a method called respiratory-space clustering, in which individual differences in the respiratory rate are effectively exploited to accurately resolve the echoes from human bodies. The proposed respiratory-space clustering can separate echoes, even when people are located close to each other. In addition, the proposed method can be applied when the number of targets is unknown and can accurately estimate the number and positions of people. We perform multiple experiments involving five or seven participants to verify the performance of the proposed method, and quantitatively evaluate the estimation accuracy for the number of people and the respiratory intervals. The experimental results show that the average root-mean-square error in estimating the respiratory interval is 196 ms using the proposed method. The use of the proposed method, rather the conventional method, improves the accuracy of the estimation of the number of people by 85.0%, which indicates the effectiveness of the proposed method for the measurement of the respiration of multiple people.

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arXiv2021

Multiradar Data Fusion for Respiratory Measurement of Multiple People

This study proposes a data fusion method for multiradar systems to enable measurement of the respiration of multiple people located at arbitrary positions. Using the proposed method, the individual respiration rates of multiple people can be measured, even when echoes from some of these people cannot be received by one of the radar systems because of shadowing. In addition, the proposed method does not require information about the positions and orientations of the radar systems used because the method can estimate the layout of these radar systems by identifying multiple human targets that can be measured from different angles using multiple radar systems. When a single target person can be measured using multiple radar systems simultaneously, the proposed method selects an accurate signal from among the multiple signals based on the spectral characteristics. To verify the effectiveness of the proposed method, we performed experiments based on two scenarios with different layouts that involved seven participants and two radar systems. Through these experiments, the proposed method was demonstrated to be capable of measuring the respiration of all seven people by overcoming the shadowing issue. In the two scenarios, the average errors of the proposed method in estimating the respiration rates were 0.33 and 1.24 respirations per minute (rpm), respectively, thus demonstrating accurate and simultaneous respiratory measurements of multiple people using the multiradar system.

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arXiv2020

Ultra-Wideband Radar and Wireless Human Sensing

This study presents the progress of our recent research regarding wireless measurements of the human body. First, we explain radar imaging algorithms for screening passengers at security checkpoints that can process data faster than existing algorithms to generate accurate and high-resolution images of human bodies. Second, we introduce signal processing techniques for accurately measuring radar micro-Doppler signals associated with human motions and activities. Finally, we present radar signal processing algorithms for measuring human respiration, heartbeat, and other types of motion. Such algorithms can be applied in various fields including healthcare, security, biometric identification, and man-machine interfaces. These fields have one thing in common: prior knowledge about the human shape, motion, and physiology are effectively exploited to improve performance by optimizing the radar systems and signal processing algorithms.

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arXiv2020

Signal Separation Using a Mathematical Model of Physiological Signals for the Measurement of Heart Pulse Wave Propagation With Array Radar

The arterial pulse wave, which propagates along the artery, is an important indicator of various cardiovascular diseases. By measuring the displacement at multiple parts of the human body, pulse wave velocity can be estimated from the pulse transit time. This paper proposes a technique for signal separation using an antenna array, so that pulse wave propagation can be measured in a non-contact manner. The body displacements due to the pulse wave at different body parts are highly correlated, and cannot be accurately separated using techniques that assume independent or uncorrelated signals. The proposed method formulates the signal separation as an optimization problem, based on a mathematical model of the arterial pulse wave. The objective function in the optimization comprises four terms that are derived based on a small-displacement approximation, unimodal impulse response approximation, and a causality condition. The optimization process was implemented using a genetic algorithm. The effectiveness of the proposed method is demonstrated through numerical simulations and experiments.

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arXiv2020

Personal Identification Using Ultrawideband Radar Measurement of Walking and Sitting Motions and a Convolutional Neural Network

This study proposes a personal identification technique that applies machine learning with a two-layered convolutional neural network to spectrogram images obtained from radar echoes of a target person in motion. The walking and sitting motions of six participants were measured using an ultrawideband radar system. Time-frequency analysis was applied to the radar signal to generate spectrogram images containing the micro-Doppler components associated with limb movements. A convolutional neural network was trained using the spectrogram images with personal labels to achieve radar-based personal identification. The personal identification accuracies were evaluated experimentally to demonstrate the effectiveness of the proposed technique.

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