Doppler Radar Techniques for Accurate Respiration Characterization and Subject Identification

Rahman, Ashikur; Lubecke, Victor M.; Borić–Lubecke, Olga; Prins, Jan H.; Sakamoto, Takuya

doi:10.1109/jetcas.2018.2818181

Cited by 68 publications

(71 citation statements)

References 11 publications

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“…Medical devices can improve the quality of life by providing a health status to assist a patient by diagnosing and treating the patient's health condition. The Doppler radar, in particular, has been widely used as a promising medical device for detecting vital signs [1][2][3][4]. The Doppler radar can diagnose the health condition by detecting the movement of the human body surface.…”

Section: Introductionmentioning

confidence: 99%

Numerical Study on the Feasibility of a 24 GHz ISM-Band Doppler Radar Antenna for Near-Field Sensing of Human Respiration in Electromagnetic Aspects

Park

Kim

et al. 2020

Applied Sciences

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The feasibility study of a 24 GHz industrial, scientific, and medical (ISM) band Doppler radar antenna in electromagnetic aspects is numerically performed for near-field sensing of human respiration. The Doppler radar antenna consists of a transmitting (Tx) antenna and a receiving (Rx) antenna close to the human body for a wearable device. The designed slot-type Doppler radar antenna is embedded between an RO4350B superstrate and an FR-4 substrate. To obtain the higher radiation pattern of the antenna towards the human body, a ground plane reflector is placed underneath the substrate. The measured −10 dB reflection coefficient (S11) bandwidth is 23.74 to 25.56 GHz and the mutual coupling (S21) between Tx and Rx antennas is lower than −30 dB at target frequencies. The Doppler radar performance of the proposed Doppler radar antenna is performed numerically by investigating the signal returned from the human body. The Doppler effect due to human respiration is investigated through the I/Q and arctangent demodulation of the returned signal. According to the results, the phase variation of the returned signal is proportional to the displacement of the body surface, which is about 0.8 rad in accordance with 1 mm displacement. The numerical experiments indicate that the proposed Doppler radar antenna can be used for near-field sensing of human respiration in electromagnetic aspects.

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Section: Introductionmentioning

confidence: 99%

Numerical Study on the Feasibility of a 24 GHz ISM-Band Doppler Radar Antenna for Near-Field Sensing of Human Respiration in Electromagnetic Aspects

Park

Kim

et al. 2020

Applied Sciences

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“…Motion sensing with Soli [35], a tiny radar chip to detect and recognize hand gestures developed by Google has now been commercialized and integrated into Google's new smartphone Pixel 4 [126]. Rahman [39] proposed yet another contact-free measurement of respiration rate by leveraging the phase shift in Doppler radar signal caused by the chest movement and allow person identification based on the subtle body kinematics of six individuals. A 2.4 GHz quadrature system is used to reduce the DC offset to allow more amplification and thus increasing the dynamic range of detection.…”

Section: ) Radar Sensorsmentioning

confidence: 99%

Sensing Technology for Human Activity Recognition: A Comprehensive Survey

et al. 2020

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Sensors are devices that quantify the physical aspects of the world around us. This ability is important to gain knowledge about human activities. Human Activity recognition plays an import role in people's everyday life. In order to solve many human-centered problems, such as health care, and individual assistance, the need to infer various simple to complex human activities is prominent. Therefore, having a well defined categorization of sensing technology is essential for the systematic design of human activity recognition systems. By extending the sensor categorization proposed by White, we survey the most prominent research works that utilize different sensing technologies for human activity recognition tasks. To the best of our knowledge, there is no thorough sensor-driven survey that considers all sensor categories in the domain of human activity recognition with respect to the sampled physical properties, including a detailed comparison across sensor categories. Thus, our contribution is to close this gap by providing an insight into the state-of-the-art developments. We identify the limitations with respect to the hardware and software characteristics of each sensor category and draw comparisons based on benchmark features retrieved from the research works introduced in this survey. Finally, we conclude with general remarks and provide future research directions for human activity recognition within the presented sensor categorization. INDEX TERMS Sensor categorization, human activity recognition, public databases for human activity recognition, physical sensors, sensor benchmark.

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“…In recent years, micro-Doppler classification has been exploited in a variety of applications, including border control and security, monitoring activities of daily living, sensing for smart environments, intruder detection, assisted living, and man-machine interfaces via gesture recognition. The primary focus of assisted living and remote health applications has been fall detection, [1][2][3][4][5] gait abnormality recognition, 6 concussion detection, 7 physical therapy and rehabilitation, 8 non-contact measurement 9 of heart rate 10, 11 and respiration, 12,13 as well as detection of related conditions, such as sleep apnea 14 or sudden infant death syndrome. 15 In all the aforementioned applications, Deep Neural Networks (DNNs) have been an enabling technology in the advancement of radar micro-Doppler classification algorithms.…”

Section: Introductionmentioning

confidence: 99%

Cross-frequency training with adversarial learning for radar micro-Doppler signature classification (Rising Researcher)

Gürbüz

Rahman

Kurtoğlu

et al. 2020

Radar Sensor Technology XXIV

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Deep neural networks have become increasingly popular in radar micro-Doppler classification; yet, a key challenge, which has limited potential gains, is the lack of large amounts of measured data that can facilitate the design of deeper networks with greater robustness and performance. Several approaches have been proposed in the literature to address this problem, such as unsupervised pre-training and transfer learning from optical imagery or synthetic RF data. This work investigates an alternative approach to training which involves exploitation of "datasets of opportunity"-micro-Doppler datasets collected using other RF sensors, which may be of a different frequency, bandwidth or waveform-for the purposes of training. Specifically, this work compares in detail the cross-frequency training degradation incurred for several different training approaches and deep neural network (DNN) architectures. Results show a 70% drop in classification accuracy when the RF sensors for pre-training, fine-tuning, and testing are different, and a 15% degradation when only the pre-training data is different, but the fine-tuning and test data are from the same sensor. By using generative adversarial networks (GANs), a large amount of synthetic data is generated for pre-training. Results show that cross-frequency performance degradation is reduced by 50% when kinematically-sifted GAN-synthesized signatures are used in pre-training.

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Doppler Radar Techniques for Accurate Respiration Characterization and Subject Identification

Cited by 68 publications

References 11 publications

Numerical Study on the Feasibility of a 24 GHz ISM-Band Doppler Radar Antenna for Near-Field Sensing of Human Respiration in Electromagnetic Aspects

Numerical Study on the Feasibility of a 24 GHz ISM-Band Doppler Radar Antenna for Near-Field Sensing of Human Respiration in Electromagnetic Aspects

Sensing Technology for Human Activity Recognition: A Comprehensive Survey

Cross-frequency training with adversarial learning for radar micro-Doppler signature classification (Rising Researcher)

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