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2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC)最新文献

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Activities Recognition and Fall Detection in Continuous Data Streams Using Radar Sensor 基于雷达传感器的连续数据流中的活动识别和跌倒检测
2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC) Pub Date : 2019-07-29 DOI: 10.1109/IMBIOC.2019.8777855
Haobo Li, Aman Shrestha, H. Heidari, J. Kernec, F. Fioranelli
{"title":"Activities Recognition and Fall Detection in Continuous Data Streams Using Radar Sensor","authors":"Haobo Li, Aman Shrestha, H. Heidari, J. Kernec, F. Fioranelli","doi":"10.1109/IMBIOC.2019.8777855","DOIUrl":"https://doi.org/10.1109/IMBIOC.2019.8777855","url":null,"abstract":"This student paper presents a Quadratic-kernel Support Vector Machine (SVM) based FMCW (Frequency Modulated Continuous Wave) radar system to recognize daily activities and detect fall accidents. Data collected in this work is divided into two different collection modes, namely, snapshots mode (different activities individually collected in isolation) and continuous activity mode (continuous streams of activities collected one after the other). For the continuous activity streams, a sliding window approach with 4s duration and 70% overlapping has achieved 84.7% classification accuracy and subsequent improvement of 2.6% has been proved by using Sequential Forward Selection (SFS) on six participants to identify an optimal feature set. A ‘tracking’ graph has been utilized to verify that the radar system can correctly identify falls as critical events among the other activities.","PeriodicalId":171472,"journal":{"name":"2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130476382","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 15
Conditional Random Field Feature Generation of Smart Home Sensor Data using Random Forests 使用随机森林生成智能家居传感器数据的条件随机场特征
2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC) Pub Date : 2019-07-29 DOI: 10.1109/IMBIOC.2019.8777764
M. Eastwood, A. Konios, Bo Tan, Yanguo Jing, Abdul Hamid
{"title":"Conditional Random Field Feature Generation of Smart Home Sensor Data using Random Forests","authors":"M. Eastwood, A. Konios, Bo Tan, Yanguo Jing, Abdul Hamid","doi":"10.1109/IMBIOC.2019.8777764","DOIUrl":"https://doi.org/10.1109/IMBIOC.2019.8777764","url":null,"abstract":"A typical approach to building a feature set for a conditional random field model is to build a large set of conjunctions of atomic tests, all of which adhere to a small number of relatively simple templates. Building more complex features in this way can be difficult, as the more complex templates needed to do this can result in a combinatoric explosion in the number of features. We use the inherent instability of decision trees to produce a small set of more complex conjunctions that are particularly suitable for the problem to be solved, using the same techniques used in generating random forest ensemble classifiers, and build a CRF on these features. We apply this method to an activity recognition problem on a dataset from the CASAS smart home project, in which we predict activities of daily living from sensor activations.","PeriodicalId":171472,"journal":{"name":"2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC)","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132195311","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Effect of Virtual Reality on Fear Emotion Base on EEG Signals Analysis 基于脑电信号分析的虚拟现实对恐惧情绪的影响
2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC) Pub Date : 2019-05-06 DOI: 10.1109/IMBIOC.2019.8777884
K. Guo, Junming Huang, Yicai Yang, Xiangmin Xu
{"title":"Effect of Virtual Reality on Fear Emotion Base on EEG Signals Analysis","authors":"K. Guo, Junming Huang, Yicai Yang, Xiangmin Xu","doi":"10.1109/IMBIOC.2019.8777884","DOIUrl":"https://doi.org/10.1109/IMBIOC.2019.8777884","url":null,"abstract":"Virtual Reality (VR), which becomes more and more common in daily life, can affect human's emotion with its special visual environment. VR has been a tool to study emotion in psychology. At the same time, the study of emotion requires an objective indicator to avoid subjective judgment. One of the methods is to measure the EEG of humans, which reflects the real situation of the brain. In order to study the effects of VR on fear emotion, we conducted EEG data collection based on the VR roller coaster scene to stimulate fear. However, there exists severe artifacts in EEG data especially when the tester is opening eyes, which blocks the correct analysis of EEG. In this paper, we propose a new artifacts removal algorithm, named WpdAI-ICA, which performs better than relevant methods. With the recovered EEG data, we compare and analyze it in time and frequency domains, and show how VR affect emotion.","PeriodicalId":171472,"journal":{"name":"2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114628016","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 2
Noncontact Measurement of Human Vital Signs during Sleep Using Low-power Millimeter-wave Ultrawideband MIMO Array Radar 利用低功率毫米波超宽带MIMO阵列雷达非接触测量睡眠中人体生命体征
2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC) Pub Date : 2019-05-06 DOI: 10.1109/IMBIOC.2019.8777864
T. Sakamoto
{"title":"Noncontact Measurement of Human Vital Signs during Sleep Using Low-power Millimeter-wave Ultrawideband MIMO Array Radar","authors":"T. Sakamoto","doi":"10.1109/IMBIOC.2019.8777864","DOIUrl":"https://doi.org/10.1109/IMBIOC.2019.8777864","url":null,"abstract":"Radar-based noncontact measurements of human bodies have increasingly been attracting attention in healthcare applications because such measurements allow for the unobtrusive sensing of vital signs without sensors being attached to the body. In particular, measurement of the heart rate is considered to be important because it provides various types of information on the physical and mental health of the person under test. In such applications, it is preferable to use low-gain omni-directional antennas so that the person under test does not have to be located at a specific spot. In addition, the use of high-power microwaves is not allowed in many countries owing to public health regulations. Because of such factors, the signal-to-noise ratio is relatively low in these applications although the target person might be within a few meters of the antennas. To overcome this difficulty, we adopt the maximum ratio combining technique with a multiple-input multiple-output antenna array for improving the signal-to-noise ratio and accuracy in measuring the instantaneous heart rate.","PeriodicalId":171472,"journal":{"name":"2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130872368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 16
Microdosimetry of Multi Electrodes Array in an RF Exposure System for In vitro Real-Time Recordings 体外实时记录射频暴露系统中多电极阵列的微剂量测定
2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC) Pub Date : 2019-05-06 DOI: 10.1109/IMBIOC.2019.8777765
A. Nefzi, C. E. Lemercier, Corinne El Khoueiry, N. Lewis, I. Lagroye, C. Boucsein, P. Lévêque, D. Arnaud-Cormos
{"title":"Microdosimetry of Multi Electrodes Array in an RF Exposure System for In vitro Real-Time Recordings","authors":"A. Nefzi, C. E. Lemercier, Corinne El Khoueiry, N. Lewis, I. Lagroye, C. Boucsein, P. Lévêque, D. Arnaud-Cormos","doi":"10.1109/IMBIOC.2019.8777765","DOIUrl":"https://doi.org/10.1109/IMBIOC.2019.8777765","url":null,"abstract":"The ubiquitous use of wireless communication devices increases the concerns about the effects of radiofrequency (RF) electromagnetic (EM) fields on human bodies. Numerous investigations concern the effects of RF exposure on the central nervous system specifically on brain cells/neurons. Thus, for recording the electrical activity of neuronal networks, an exposure device based on an open transverse electromagnetic (TEM) cell containing a multi electrode array (MEA) was designed and characterized. The new MEA was designed to improve the electromagnetic compatibility with the RF exposure device. The exposure system operates in the frequency band 0.1 – 2 GHz with a reflection coefficient S11 under −10 dB. The TEM cell exposure device was characterized at 1.8 GHz to assess the propagating E-field when inserting the MEA.","PeriodicalId":171472,"journal":{"name":"2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133461141","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Emotion Recognition Using Frontal EEG in VR Affective Scenes 基于额叶脑电图的VR情感场景情感识别
2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC) Pub Date : 2019-05-06 DOI: 10.1109/IMBIOC.2019.8777843
Tianyuan Xu, Rui-Xiang Yin, Lin Shu, Xiangmin Xu
{"title":"Emotion Recognition Using Frontal EEG in VR Affective Scenes","authors":"Tianyuan Xu, Rui-Xiang Yin, Lin Shu, Xiangmin Xu","doi":"10.1109/IMBIOC.2019.8777843","DOIUrl":"https://doi.org/10.1109/IMBIOC.2019.8777843","url":null,"abstract":"Frontal EEG has been widely used for human emotion recognition since its convenience. However, many relevant studies used traditional wet electrodes to collect EEG signals and the stimulation ways were restricted as music, videos and pictures. This paper provides a new framework for emotion recognition using frontal EEG and VR affective scenes. An experiment about VR stimuli EEG data collection was conducted among 19 subjects. The EEG data were collected using textile dry electrodes. EEG features were extracted from time, frequency and space domain in the collected data. Model stacking method were applied in the experiment to ensemble 3 models including GBDT, RF and SVM. The mean accuracy of our framework achieved about 81.30%, which exhibited better performance compared with relevant studies. The framework proposed in this work can be well applied to wearable device for EEG emotion recognition in VR scenes.","PeriodicalId":171472,"journal":{"name":"2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC)","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130335447","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 15
Multi-scale Convolution and Feature-weighting Network for Radar Target Recognition 雷达目标识别的多尺度卷积和特征加权网络
2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC) Pub Date : 2019-05-06 DOI: 10.1109/IMBIOC.2019.8777825
Chenchen Wang, W. Su, Hong Gu, Jianchao Yang
{"title":"Multi-scale Convolution and Feature-weighting Network for Radar Target Recognition","authors":"Chenchen Wang, W. Su, Hong Gu, Jianchao Yang","doi":"10.1109/IMBIOC.2019.8777825","DOIUrl":"https://doi.org/10.1109/IMBIOC.2019.8777825","url":null,"abstract":"Target recognition is one of the most significant applications of synthetic aperture radar (SAR). However, satisfactory results are impractical to achieve by human effort alone due to the continual and rapid growth of the quantity of radar data. In view of the great success of convolutional neural networks (CNNs) in optical image classification tasks, in this paper, we apply a modified CNN to improve the classification accuracy. Instead of simply stacking several convolutional, pooling and activation layers to build a structure, a module that groups three different forms of convolution is designed to improve the feature extraction ability. Considering the complexity of SAR image composition, targets are not adequately described with single-scale feature maps. In addition, to utilize the correlation of features, an extra module is designed to measure the weights of the features and preprocess the input of the next stage. Experiments are performed on a moving and stationary target acquisition and recognition dataset. The proposed method achieves an average accuracy of 98% and a maximum accuracy of 99.67%, which demonstrates its efficiency compared with existing methods.","PeriodicalId":171472,"journal":{"name":"2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134163254","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Research on Imaging Algorithm of Millimeter Wave Radar Based on Stolt Interpolation 基于Stolt插值的毫米波雷达成像算法研究
2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC) Pub Date : 2019-05-06 DOI: 10.1109/IMBIOC.2019.8777751
Shu Jiacheng, Miao Chen
{"title":"Research on Imaging Algorithm of Millimeter Wave Radar Based on Stolt Interpolation","authors":"Shu Jiacheng, Miao Chen","doi":"10.1109/IMBIOC.2019.8777751","DOIUrl":"https://doi.org/10.1109/IMBIOC.2019.8777751","url":null,"abstract":"The paper presents an imaging algorithm of millimeter wave radar. The radar scans the space by changing the antenna positions and acquires the signals at different frequencies in each point. The stolt interpolation algorithm converses $k_{x}^{prime}-f$ domain to $k_{x}^{prime}-k_{z}^{prime}$ domain. Then by inverse Fourier transform we obtain the image of the object in the space plane. Finally, the effectiveness of the algorithm is verified by MATLAB simulation.","PeriodicalId":171472,"journal":{"name":"2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129565439","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 5
A Compact Microwave Applicator for the Rapid Detection of Clostridium Difficile 一种用于艰难梭菌快速检测的小型微波涂布器
2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC) Pub Date : 2019-05-06 DOI: 10.1109/IMBIOC.2019.8777882
H. Hamzah, E. Ahortor, D. Malyshev, H. Choi, J. Lees, L. Baillie, A. Porch
{"title":"A Compact Microwave Applicator for the Rapid Detection of Clostridium Difficile","authors":"H. Hamzah, E. Ahortor, D. Malyshev, H. Choi, J. Lees, L. Baillie, A. Porch","doi":"10.1109/IMBIOC.2019.8777882","DOIUrl":"https://doi.org/10.1109/IMBIOC.2019.8777882","url":null,"abstract":"Clostridium difficile is a major cause of hospital acquired infection that poses significant diagnostic challenges. In this paper, a new type of resonant microwave applicator is proposed for the liberation of DNA from C. difficile spores via microwave disruption, followed by rapid detection using a sandwich hybridization assay. A split-ring resonator is designed to operate at 2.4 GHz with a 3 mm active gap region that contains an isolate volume of approximately 10 mm3, exposed to pulsed microwaves of 12 W rms power. The parallel electric field configuration maximizes the interaction between the microwaves and the sample. In a proof of principle study, in combination with pathogen specific DNA probes, we have used the system to correctly identify virulent strains of C. difficile using magnetic bead extraction of DNA suitable for point-of-care application.","PeriodicalId":171472,"journal":{"name":"2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC)","volume":"233 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133350495","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 1
Simultaneous Detection of Multi-Target Vital Signs Using EEMD Algorithm Based on FMCW Radar 基于FMCW雷达的EEMD算法多目标生命体征同时检测
2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC) Pub Date : 2019-05-06 DOI: 10.1109/IMBIOC.2019.8777810
Guan-Wei Fang, Ching-Yao Huang, Chin-Lung Yang
{"title":"Simultaneous Detection of Multi-Target Vital Signs Using EEMD Algorithm Based on FMCW Radar","authors":"Guan-Wei Fang, Ching-Yao Huang, Chin-Lung Yang","doi":"10.1109/IMBIOC.2019.8777810","DOIUrl":"https://doi.org/10.1109/IMBIOC.2019.8777810","url":null,"abstract":"This paper presents a novel approach to simultaneously monitor multi-target vital-signs using a frequency modulation continuous wave (FMCW) radar within the resolution limitation. For a traditional system architecture on multi-target vital-signs monitoring, complicated systems are required such as phased array radar or continuous wave (CW) radar with beamforming technology. In contrast, this architecture has the advantage of enhanced resolution capability, relatively simple circuit, and low cost. By using advanced signal processing such as adaptive boundary, we can detect multi-target vital signs even though the difference of the distances to the two targets is less than the range resolution of FMCW radar. In terms of demodulation, heart rate (HR) is susceptible to the harmonic of respiratory rate (RR) using complex signal demodulation (CSD). Therefore, this paper uses an ensemble empirical mode decomposition (EEMD) algorithm to extract the intrinsic mode functions of RR and HR. Experiments show that, we can improve signal-to-noise ratio (SNR) and accuracy significantly using this algorithm. And the vital sign errors of the two targets separated at 70 cm and 50 cm are averagely 2.35% and 4.44%, respectively.","PeriodicalId":171472,"journal":{"name":"2019 IEEE MTT-S International Microwave Biomedical Conference (IMBioC)","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115169616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 16
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