scholarly journals Radar Recorded Child Vital Sign Public Dataset and Deep Learning-Based Age Group Classification Framework for Vehicular Application

Sensors ◽  
2021 ◽  
Vol 21 (7) ◽  
pp. 2412
Author(s):  
Sungwon Yoo ◽  
Shahzad Ahmed ◽  
Sun Kang ◽  
Duhyun Hwang ◽  
Jungjun Lee ◽  
...  
Keyword(s):  
Vital Sign ◽  
Continuous Wave ◽  
Age Groups ◽  
Vital Signs ◽  
Group Classification ◽  
Age Group ◽  
Smart Vehicles ◽  
Fmcw Radar ◽  
Wide Range ◽  
Age Group Classification

The ongoing intense development of short-range radar systems and their improved capability of measuring small movements make these systems reliable solutions for the extraction of human vital signs in a contactless fashion. The continuous contactless monitoring of vital signs can be considered in a wide range of applications, such as remote healthcare solutions and context-aware smart sensor development. Currently, the provision of radar-recorded datasets of human vital signs is still an open issue. In this paper, we present a new frequency-modulated continuous wave (FMCW) radar-recorded vital sign dataset for 50 children aged less than 13 years. A clinically approved vital sign monitoring sensor was also deployed as a reference, and data from both sensors were time-synchronized. With the presented dataset, a new child age-group classification system based on GoogLeNet is proposed to develop a child safety sensor for smart vehicles. The radar-recorded vital signs of children are divided into several age groups, and the GoogLeNet framework is trained to predict the age of unknown human test subjects.

scholarly journals Establishment of age group classification for risk stratification in glioma patients

2020 ◽  
Author(s):  
Zhiying Lin ◽  
Runwei Yang ◽  
Yawei Liu ◽  
Kaishu Li ◽  
Guozhong Yi ◽  
...  
Keyword(s):  
Molecular Markers ◽  
Risk Stratification ◽  
Tumor Size ◽  
Age Groups ◽  
Tumor Location ◽  
Group Classification ◽  
Age Group ◽  
Ki 67 ◽  
Who Grade ◽  
Age Group Classification

Abstract Objective: Age is associated with the prognosis of glioma patients, but there is no uniform standard of age-group classification to evaluate the prognosis of glioma patients. In this study, we aimed to establish an age group classification for risk stratification in glioma patients. Methods: A total of 1502 patients diagnosed with gliomas at Nanfang Hospital between 2000 and 2018 were enrolled. The WHO grade of glioma was used as a dependent variable to evaluate the effect of age on risk stratification. The evaluation model was established by logistic regression, and the Akaike information criterion (AIC) value of the model was used to determine the optimal cutoff points for age-classification. The differences in gender, WHO grade, pathological subtype, tumor cell differentiation direction, tumor size, tumor location, and molecular markers between different age groups were analyzed. The molecular markers included GFAP, EMA, MGMT, p53, NeuN, Oligo2, EGFR, VEGF, IDH1, Ki-67, 1p/19q, PR, CD3, H3K27M, and TS. Results: The proportion of men with glioma was higher than that of women with glioma (58.3% vs 41.7%). Analysis of age showed that appropriate classifications of age group were 0-14 years old (pediatric group), 15-47 years old (youth group), 48-63 years old (middle-aged group), and ≥64 years old (elderly group).The proportions of glioblastoma and large tumor size (4-6 cm) increased with age (p = 0.000, p = 0.018, respectively ). Analysis of the pathological molecular markers across the four age groups showed that the proportion of patients with larger than 10% area of Ki-67 expression or positive PR expression increased with age (p = 0.000, p = 0.017, respectively). Conclusion: Age was effective evaluating the risk of glioblastoma in glioma patients. Appropriate classifications of age group for risk stratification were 0-14 years old (pediatric group), 15-47 years old (young group), 48-63 years old (middle age group) and ≥ 64 years old (elderly group). There was significant heterogeneity in WHO grade, tumor size, tumor location and some molecular markers among the four age groups.

scholarly journals Vital Sign Monitoring Using FMCW Radar in Various Sleeping Scenarios

Sensors ◽  
2020 ◽  
Vol 20 (22) ◽  
pp. 6505
Author(s):  
Emmi Turppa ◽  
Juha M. Kortelainen ◽  
Oleg Antropov ◽  
Tero Kiuru
Keyword(s):  
Heart Rate ◽  
Remote Monitoring ◽  
Vital Sign ◽  
Continuous Wave ◽  
Real Life ◽  
Vital Signs ◽  
Absolute Error ◽  
Physical Contact ◽  
Radar Signal ◽  
Fmcw Radar

Remote monitoring of vital signs for studying sleep is a user-friendly alternative to monitoring with sensors attached to the skin. For instance, remote monitoring can allow unconstrained movement during sleep, whereas detectors requiring a physical contact may detach and interrupt the measurement and affect sleep itself. This study evaluates the performance of a cost-effective frequency modulated continuous wave (FMCW) radar in remote monitoring of heart rate and respiration in scenarios resembling a set of normal and abnormal physiological conditions during sleep. We evaluate the vital signs of ten subjects in different lying positions during various tasks. Specifically, we aim for a broad range of both heart and respiration rates to replicate various real-life scenarios and to test the robustness of the selected vital sign extraction methods consisting of fast Fourier transform based cepstral and autocorrelation analyses. As compared to the reference signals obtained using Embla titanium, a certified medical device, we achieved an overall relative mean absolute error of 3.6% (86% correlation) and 9.1% (91% correlation) for the heart rate and respiration rate, respectively. Our results promote radar-based clinical monitoring by showing that the proposed radar technology and signal processing methods accurately capture even such alarming vital signs as minimal respiration. Furthermore, we show that common parameters for heart rate variability can also be accurately extracted from the radar signal, enabling further sleep analyses.

scholarly journals Age Group Classification using Convolutional Neural Network (CNN)

2021 ◽  
Vol 2084 (1) ◽  
pp. 012028
Author(s):  
Muhammad Firdaus Mustapha ◽  
Nur Maisarah Mohamad ◽  
Ghazali Osman ◽  
Siti Haslini Ab Hamid
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Age Groups ◽  
Group Classification ◽  
Classification Model ◽  
Age Group ◽  
Health Security ◽  
The Face ◽  
Age Group Classification ◽  
Two Age Groups

Abstract Age group classification is a complex task that is used to classify facial images or videos into predetermined age categories. It is an important task due to its numerous applications such as health, security, authentication system, recruitment, and also in intelligent social robots. Convolutional Neural Network (CNN) has recently shown excellent performance in analysing human face images and videos. This paper proposed an age group classification task using CNN that trained and tested with an All-Age Face (AAF) dataset. FaceNet deep learning model that uses CNN was applied in this study to compute a 128-d embedding that quantifies the face of the age group. The experiment included two age groups: Adolescence and Mature Adulthood. The proposed age group classification model achieved 84.90% accuracy for the training images and 85.12% accuracy for the test images. The experimental results showed that CNN is capable of achieving competitive classification accuracy throughout two age groups in the AAF dataset with unbalanced data distribution.

scholarly journals Age Group Classification of Facial Images Using Rank Based Edge Texture Unit (RETU) and Fuzzy Texture

2019 ◽  
pp. 605-611
Author(s):  
Jammula Nimitha ◽  
Kuraganti Nukeswari ◽  
Kuraganti Sudha ◽  
Kurapati Sumithra ◽  
Rama Devi Gunnam
Keyword(s):  
Age Groups ◽  
Group Classification ◽  
Facial Features ◽  
Age Group ◽  
Human Beings ◽  
Age Group Classification ◽  
Facial Images

We as human beings can estimate the age of a person based on his facial features but there are situations where there is a need for the computers to determine the age of a person based on the picture or photograph. Here comes the situation to teach a machine to determine the age group of a person with his picture. This is applicable in the fields like determining the age of a criminal with his picture or determining the age of a patient when he has undergone an accident and many other fields. To address this problem the paper proposed a technique of finding the age with Rank Based Edge Texture Unit (RETU). The uniqueness of this method is that it divides the age group into 7 classes i.e. the age groups are 1-10, 11-20, 21-30,31-0,41-50,51-60,>60 . With this method, the results cope up to 97.16% and to slightly increase the efficiency the present paper proposes to add Fuzzy Texton features.

scholarly journals Selecting Target Range with Accurate Vital Sign Using Spatial Phase Coherency of FMCW Radar

2021 ◽  
Vol 11 (10) ◽  
pp. 4514
Author(s):  
Ho-Ik Choi ◽  
Woo-Jin Song ◽  
Heemang Song ◽  
Hyun-Chool Shin
Keyword(s):  
Vital Sign ◽  
Continuous Wave ◽  
Physiological State ◽  
Vital Signs ◽  
Target Range ◽  
Human Beings ◽  
Fmcw Radar ◽  
Spatial Phase ◽  
Human Respiration ◽  
Human Body Surface

Respiration and heartbeat are basic indicators of the physiological state of human beings. Frequency-modulated continuous wave (FMCW) radar can sense micro-displacement in the human body surface without contact, and is used for vital-sign (respiration and heartbeat) monitoring. For the extraction of vital-sign, it is essential to select the target range containing vital-sign information. In this paper, we exploit the coherency of phase in different range-bins of FMCW radar to effectively select the range-bins that contain accurate signals for remote monitoring of human respiration and heartbeat. To quantify coherency, the spatial phase coherency (SPC) index is introduced. The experimental results show that the SPC can select a range-bin containing more accurate vital-sign signals than conventional methods. This result demonstrates that the proposed method is accurate for monitoring of vital signs by using FMCW radar.

scholarly journals A cross-sectional study on morbidity status among school going adolescents in Thiruvallur district, Tamil Nadu

2019 ◽  
Vol 6 (12) ◽  
pp. 5201
Author(s):  
Sathish Dev D. ◽  
Sugantha Valli M. ◽  
Gnana Sezhian M. ◽  
Suganya E.
Keyword(s):  
Tamil Nadu ◽  
Gastrointestinal Disease ◽  
Age Groups ◽  
Cross Sectional Study ◽  
Health Problems ◽  
Age Group ◽  
Sectional Study ◽  
Cross Sectional ◽  
Wide Range ◽  
Tract Infections

Background: Adolescents represent about 21.8 percent of India’s population. Various health risks with potentially life-threatening consequences become prominent in this age group. This study was undertaken with the objective to determine the morbidity profile of school going adolescents in Tamil Nadu.Methods: This descriptive, cross sectional study was planned and conducted from January 2016 to August 2017. The study population included 987 adolescent boys and girls aged between 10 to 19 years studying in high and higher secondary Government schools of Thiruvallur district of Tamil Nadu. Semi-structured questionnaire was used as data collection tool.Results: The mean age groups of this school going adolescent are 14.2 yrs. In the present study 583 (59%) of the study participants were affected by one or more morbidity condition. Among them, 395 (67.7%) were in the age group 10-14 years and 188 (32.2%) in the age group 15-19 years. 122 (21%) and 461 (79.1%) of male and female were affected respectively. In the present study, fever (21%) was the commonest reported morbidity followed by acute respiratory infection (15.7%) and acute gastrointestinal disease (13. 4%).Conclusions: This study shows that adolescents are prone to a wide range of morbidity conditions. Apart from respiratory and gastro intestinal diseases, reproductive tract infections and sexual health problems are important morbidities affecting this age group. There is strong need to sensitize health care practitioners at all levels, in both government and private sectors towards health problems in adolescent age groups.

scholarly journals FPGA Implementation of an Efficient FFT Processor for FMCW Radar Signal Processing

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6443
Author(s):  
Jinmoo Heo ◽  
Yongchul Jung ◽  
Seongjoo Lee ◽  
Yunho Jung
Keyword(s):  
Signal Processing ◽  
Detection Rate ◽  
Continuous Wave ◽  
Vital Signs ◽  
Radar Signal ◽  
Radar Signal Processing ◽  
Fmcw Radar ◽  
Design And Implementation ◽  
Fft Processor ◽  
Point Operator

This paper presents the design and implementation results of an efficient fast Fourier transform (FFT) processor for frequency-modulated continuous wave (FMCW) radar signal processing. The proposed FFT processor is designed with a memory-based FFT architecture and supports variable lengths from 64 to 4096. Moreover, it is designed with a floating-point operator to prevent the performance degradation of fixed-point operators. FMCW radar signal processing requires windowing operations to increase the target detection rate by reducing clutter side lobes, magnitude calculation operations based on the FFT results to detect the target, and accumulation operations to improve the detection performance of the target. In addition, in some applications such as the measurement of vital signs, the phase of the FFT result has to be calculated. In general, only the FFT is implemented in the hardware, and the other FMCW radar signal processing is performed in the software. The proposed FFT processor implements not only the FFT, but also windowing, accumulation, and magnitude/phase calculations in the hardware. Therefore, compared with a processor implementing only the FFT, the proposed FFT processor uses 1.69 times the hardware resources but achieves an execution time 7.32 times shorter.

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