Non-contact respiratory measurement in a horse in standing position using millimeter-wave array radar

飼育ウマの呼吸数を非接触で測定することに成功 --ミリ波レーダを用いた非接触バイタル測定技術の確立へ--. 京都大学プレスリリース. 2022-08-10.This study aimed to apply radar technology to a large quadruped animal. We first developed a non-contact respiration measurement system using millimeter-wave array radar for a horse in standing position. Specifically, we measured the respiration of a stationary domestic horse in stables. Simultaneously, we measured the respiration rate using infrared thermography and developed a method for analyzing the radar information while verifying the rate of agreement. Our results suggested that the radar technology detected breathing and accurately measured the respiration of a horse, despite variation in the breathing frequency. To the best of our knowledge, this is the first study to apply a non-contact respiration measurement system using millimeter-wave array radar has been applied to large animals in an upright position, thereby demonstrating its potential application in animal husbandry and welfare

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

Radar-Based Automatic Detection of Sleep Apnea Using Support Vector Machine

2020 International Symposium on Antennas and Propagation (ISAP), 25-28 Jan. 2021, Osaka, JapanEarly diagnosis of sleep-apnea-related breathing problems helps to avoid the increased risk they can cause. In this study, we performed simultaneous radar measurements and polysomnography on patients with sleep apnea. A support vector machine algorithm was applied to the radar data to automatically detect sleep apnea events. Support vector machine parameters were optimized using the relationship between the radar and polysomnography data. The support vector machine was found to be effective in noncontact detection of central/mixed sleep apnea events using radar data. The proposed approach achieved an accuracy of 79.5%, a recall of 71.2%, and a precision of 71.2%

Noncontact Detection of Sleep Apnea Using Radar and Expectation-Maximization Algorithm

Sleep apnea syndrome requires early diagnosis because this syndrome can lead to a variety of health problems. If sleep apnea events can be detected in a noncontact manner using radar, we can then avoid the discomfort caused by the contact-type sensors that are used in conventional polysomnography. This study proposes a novel radar-based method for accurate detection of sleep apnea events. The proposed method uses the expectation-maximization algorithm to extract the respiratory features that form normal and abnormal breathing patterns, resulting in an adaptive apnea detection capability without any requirement for empirical parameters. We conducted an experimental quantitative evaluation of the proposed method by performing polysomnography and radar measurements simultaneously in five patients with the symptoms of sleep apnea syndrome. Through these experiments, we show that the proposed method can detect the number of apnea and hypopnea events per hour with an error of 4.8 times/hour; this represents an improvement in the accuracy by 1.8 times when compared with the conventional threshold-based method and demonstrates the effectiveness of our proposed method.Comment: 8 pages, 12 figures, 3 tables. This work is going to be submitted to the IEEE for possible publicatio

高精度医用超音波測定に向けたアレイ信号処理

京都大学0048新制・課程博士博士(情報学)甲第21218号情博第671号新制||情||116(附属図書館)京都大学大学院情報学研究科通信情報システム専攻(主査)教授 佐藤 亨, 教授 山本 衛, 教授 松田 哲也学位規則第4条第1項該当Doctor of InformaticsKyoto UniversityDGA

Evaluation of FcεRl-binding serum IgE in patients with ocular allergic diseases

We evaluated high-affinity receptor for IgE (FcεRI)- binding serum IgE in patients with atopic keratoconjunctivitis (AKC; n=31) and with seasonal allergic conjunctivitis (SAC; n=13) by enzyme-linked immunosorbent assay (ELISA) using a recombinant soluble form of the human FcεRIα ectodomain (soluble α). The quantities of FcεRI-binding IgE are compared with those of total IgE measured by a conventional sandwich ELISA. Both of the quantities of FcεRI-binding and total IgE in AKC were significantly larger than those in SAC (P<0.001). In contrast, the proportion of FcεRI- binding IgE (FcεRI-binding IgE/total IgE; %) in SAC was significantly larger than that in AKC (P <0.001), although significant reverse correlation was observed between the proportion of FcεRI-binding IgE and total IgE in both AKC and SAC. Significantly, a higher proportion of FcεRI-binding IgE in SAC than that in AKC may reflect the differences in pathologic states of AKC and SAC that are caused by a disparity in immune responses in these diseases

High-resolution Imaging and Separation of Multiple Pedestrians Using UWB Doppler Radar Interferometry with Adaptive Beamforming Technique

Ultra-wideband (UWB) radar imaging has attracted attention for use in security and intelligent transportation system (ITS) applications. Conventional UWB Doppler interferometry is an effective way to obtain high-resolution images while using a simple radar system. However, this method produces ghost images when multiple closely-spaced human targets are present. To resolve this problem, we propose a new technique that combines UWB Doppler interferometry with an adaptive beamforming method called estimation of signal parameters via rotational invariance techniques (ESPRIT). We also propose a tracking and separation algorithm that uses the k-nearest neighbor method. Through a combination of numerical simulations and measurements, we demonstrate the remarkable performance improvement that can be achieved using our proposed method. The proposed method can separate multiple humans with a root-mean-square error of 5.2 cm, which makes its accuracy 1.9 times higher than that of the conventional method

Remote heartbeat monitoring from human soles using 60-GHz ultra-wideband radar

Measurement of heartbeats is essential in cardiovascular magnetic resonance imaging because the measurement must be synchronized with the phase of cardiac cycles. Many existing studies on radar-based heartbeat monitoring have focused on echoes from the torso only, and such monitoring cannot be applied to subjects in magnetic resonance scanners because only the head and soles can be seen from the outside. In this study, we demonstrate the feasibility of the remote monitoring of heartbeats from the subject’s soles using a 60-GHz ultra-wideband radar. The heartbeat intervals measured using the radar are quantitatively compared with those measured using conventional electrocardiography