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

Yoo, Sung-Won; Ahmed, Shihab; Kang, Seok Young; Hwang, Duhyun; Lee, Jungjun; Son, Jungduck; Cho, Sung Ho

doi:10.3390/s21072412

Cited by 30 publications

(35 citation statements)

References 32 publications

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“…The output of unwrapping should be in the range of ] −π, π], which is a zero centered, 2π length interval with the modulo-2π operator [49], with no loss of generality by subtracting π radian as (12) where Φ(t) is the phase at the current time index t and Φ(t − 1) is the phase at the previous time index. After extracting and unwrapping the signal in ( 9), the phase difference in (10) is obtained, and ΔR can be calculated.…”

Section: ) Range -Fast Fourier Transformation (Fft)mentioning

confidence: 99%

“…Unfortunately, the detection with a camera had its drawbacks in terms of light and privacy. In [10], detecting vital signs using radar was used to classify the subject's age by using single range-FFT image. However, their classification method did not observe the characteristic of the breathing waveform.…”

Section: Introductionmentioning

confidence: 99%

“…Patients suffering from respiratory disorders or COVID-19 have a unique breathing characteristic that cannot be represented by heart and breath rates. Furthermore, the system in [10] had not been implemented to perform a detection in real-time. Thus, based on this background, the challenge is to develop a non-contact monitoring device that can classify breathing waveform based on the characteristics of the signal in real-time.…”

Section: Introductionmentioning

confidence: 99%

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Non-Contact Supervision of COVID-19 Breathing Behaviour With FMCW Radar and Stacked Ensemble Learning Model in Real-Time

Purnomo

Komariah

Lin

et al. 2022

IEEE Trans. Biomed. Circuits Syst.

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A respiratory disorder that attacks COVID-19 patients requires intensive supervision of medical practitioners during the isolation period. A non-contact monitoring device will be a suitable solution for reducing the spread risk of the virus while monitoring the COVID-19 patient. This study uses Frequency-Modulated Continuous Wave (FMCW) radar and Machine Learning (ML) to obtain respiratory information and analyze respiratory signals, respectively. Multiple subjects in a room can be detected simultaneously by calculating the Angle of Arrival (AoA) of the received signal and utilizing the Multiple Input Multiple Output (MIMO) of FMCW radar. Fast Fourier Transform (FFT) and some signal processing are implemented to obtain a breathing waveform. ML helps the system to analyze the respiratory signals automatically. This paper also compares the performance of several ML algorithms such as Multinomial Logistic Regression (MLR), Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), eXtreme Gradient Boosting (XGB), Light Gradient Boosting Machine (LGBM), CatBoosting (CB) Classifier, Multilayer Perceptron (MLP), and three proposed stacked ensemble models, namely Stacked Ensemble Classifier (SEC), Boosting Tree-based Stacked Classifier (BTSC), and Neural Stacked Ensemble Model (NSEM) to obtain the best ML model. The results show that the NSEM algorithm achieves the best performance with 97.1% accuracy. In the real-time implementation, the system could simultaneously Manuscript

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Section: ) Range -Fast Fourier Transformation (Fft)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Non-Contact Supervision of COVID-19 Breathing Behaviour With FMCW Radar and Stacked Ensemble Learning Model in Real-Time

Purnomo

Komariah

Lin

et al. 2022

IEEE Trans. Biomed. Circuits Syst.

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“…Contrary to an unmodulated (single Frequency) continuous wave radar, FMCW radar is capable of providing rich information about the target's range, Doppler-velocity and angle simultaneously. Consequently, FMCW radar has widely been explored previously for several applications such as vital sign monitoring [8], human gait analysis [9] and specifically, hand gesture recognition [10]. Perhaps, radar has recently shown its footprints for multiple target gesture recognition as well [11].…”

Section: Introductionmentioning

confidence: 99%

Radar-Based Air-Writing Gesture Recognition Using a Novel Multistream CNN Approach

Ahmed

Kim

Park

et al. 2022

IEEE Internet Things J.

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Hand gestures being a convenient and natural way of communication, is getting huge attention for human computer interface designs. Amongst these gestures, detecting midair writing is one of the most promising application. Existing radar based solutions often performs the mid-air writing recognition by tracking the hand trajectory using multiple monostatic or bi-static radars. This paper presents a Multi-Stream Convolutional Neural Network (MS-CNN) based in-air digits recognition method using a Frequency Modulated Continuous wave (FMCW) radar. With one FMCW radar comprising of two receiving channels, a novel three stream CNN network with Range-time, Doppler-time and Angle-time spectrograms as inputs is constructed and the features are fused together in later stage before making a final recognition. Unlike the traditional CNN, MS-CNN with multiple independent input layers enable the creation of multi-dimensional deep-learning model for FMCW radars. Twelve human volunteers were invited to writing the digit from zero to nine in the air in both home and lab environment. The three stream CNN architecture based air-writing for digits has shown a promising accuracy of 95%. Comparison of proposed MS-CNN system was made with 45 different variants of CNN and preliminary results shows that MS-CNN outperforms the other traditional CNN architectures for air-writing application. The gestures radar data has also been made available to the research community.

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“…These systems have different pros and cons during operations [230,231,232,291], thanks to their dedicated hardware systems which result in high resolution or sensing range / performance, they have been deployed for various applications such as indoor healthcare monitoring [292,293,294,295,296,297,298,257,273,274,256,258,299,275,276,239,242,244,245,271,300,254,301], in-vehicle monitoring [302,278,253,285,259,303], and continuous user authentication [304,284].…”

Section: Related Workmentioning

confidence: 99%

Tensor computing for big data analytic

Ong¹

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First and foremost, I would like to thank my supervisor, Assoc. Prof. Ng Wee Keong, for his kindness, trust, and patience throughout the years of my PhD study. The research guidance given by Prof. Ng has important influence to the success of our new discovery. The research environment in NTU SCSE provides us the research freedom to carry out meaningful explorations and experimentations with new inventions or innovations. The regular seminars held by the school and university inspires us with new ideas and expand our core research to stay up-todate and relevant. I would like to thank Assoc. Prof. Adams Kong Wai Kin, Assoc.

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

Cited by 30 publications

References 32 publications

Non-Contact Supervision of COVID-19 Breathing Behaviour With FMCW Radar and Stacked Ensemble Learning Model in Real-Time

Non-Contact Supervision of COVID-19 Breathing Behaviour With FMCW Radar and Stacked Ensemble Learning Model in Real-Time

Radar-Based Air-Writing Gesture Recognition Using a Novel Multistream CNN Approach

Tensor computing for big data analytic

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