Sleep Posture Recognition With a Dual-Frequency Cardiopulmonary Doppler Radar

Kiriazi, John E.; Islam, Shekh Md Mahmudul; Borić–Lubecke, Olga; Lubecke, Victor M.

doi:10.1109/access.2021.3062385

Cited by 34 publications

(25 citation statements)

References 47 publications

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“…The baseband signal I-Q plot provides an arc if the motion of the target is periodic and from the arc, the center can be estimated. The radius of the arc is directly proportional to the ERCS (Kiriazi et al, 2021, Islam et al, 2022. From the reflected echoes ERCS sleep postures such as supine, prone, and side can be recognized (Kiriazi et al, 2021, Islam et al, 2022.…”

Section: Radar Cross Section (Rcs) For Measuring the Shape/orientatio...mentioning

confidence: 99%

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Radar-based remote physiological sensing: Progress, challenges, and opportunities

Islam

2022

Front. Physiol.

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Modern microwave Doppler radar-based physiological sensing is playing an important role in healthcare applications and during the last decade, there has been a significant advancement in this non-contact respiration sensing technology. The advantages of contactless, unobtrusive respiration monitoring have drawn interest in various medical applications such as sleep apnea, sudden infant death syndromes (SIDS), remote respiratory monitoring of burn victims, and COVID patients. This paper provides a perspective on recent advances in biomedical and healthcare applications of Doppler radar that can detect the tiny movement of the chest surfaces to extract heartbeat and respiration and its associated different vital signs parameters (tidal volume, heart rate variability (HRV), and so on) of the human subject. Additionally, it also highlights the challenges, and opportunities of this remote physiological sensing technology and several future research directions will be laid out to deploy this sensor technology in our day-to-day life.

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Section: Radar Cross Section (Rcs) For Measuring the Shape/orientatio...mentioning

confidence: 99%

“…Frontiers in Physiology frontiersin.org sleep posture recognition and the quality of sleep can also be determined (Kiriazi et al, 2021, Islam et al, 2022. This particular non-contact sensing technology can be a potential hand-held device for sleep disorder diagnosis and intervention.…”

Section: Radar Cross Section (Rcs) For Measuring the Shape/orientatio...mentioning

confidence: 99%

Radar-based remote physiological sensing: Progress, challenges, and opportunities

Islam

2022

Front. Physiol.

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“…There are some abrupt changes in the time-domain signal (as shown in the red box in Figure 10 ). These obvious “faults” are caused by sleep posture change [ 27 ], which are excluded in the algorithm. In Figure 10 , period 1 is in the supine state, where the signal is relatively stable.…”

Section: Radar System and Experimentsmentioning

confidence: 99%

Noncontact Sleeping Heartrate Monitoring Method Using Continuous-Wave Doppler Radar Based on the Difference Quadratic Sum Demodulation and Search Algorithm

Chen¹,

Ni²

2022

Sensors

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Continuous-wave doppler radar, which has the advantages of simple structure, low cost, and low power consumption, has attracted extensive attention in the detection of human vital signs. However, while respiration and heartbeat signals are mixed in the echo phase, the amplitude difference between the two signals is so large that it becomes difficult to measure the heartrate (HR) from the interference of respiration stably and accurately. In this paper, the difference quadratic sum demodulation method is proposed. According to the mixed characteristics of respiration and heartbeat after demodulation, the heartbeat features can be extracted with the help of the easy-to-detect breathing signal; combined with the constrained nearest neighbor search algorithm, it can realize sleeping HR monitoring overnight without body movements restraint. Considering the differences in vital-sign characteristics of different individuals and the irregularity of sleep movements, 54 h of sleep data for nine nights were collected from three subjects, and then compared with ECG-based HR reference equipment. After excluding the periods of body turning over, the HR error was within 10% for more than 70% of the time. Experiments confirmed that this method, as a tool for long-term HR monitoring, can play an important role in sleeping monitoring, smart elderly care, and smart homes.

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“…While this research topic is specifically related to wearable sensing technologies, it should also be pointed out that many non-contact in-situ sensing technologies are emerging on the market. For example, non-contact stress detection is possible through infrared and microwave doppler radar ( Zakrzewski et al, 2012 ; Lee et al, 2015 ; Shan et al, 2020 ), and doppler radar may be used to non-contact measurements of the orientation (and possibly quality) of sleep ( Kiriazi et al, 2021 ). It is possible that non-contact biosensors may meet or exceed the sensitivity and reliability of wearable sensors in the future, may prove valuable complements to wearable sensors, and may increase the breadth and specificity of measurements that could be used in predictive models.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach

et al. 2021

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Wearable technologies for measuring digital and chemical physiology are pervading the consumer market and hold potential to reliably classify states of relevance to human performance including stress, sleep deprivation, and physical exertion. The ability to efficiently and accurately classify physiological states based on wearable devices is improving. However, the inherent variability of human behavior within and across individuals makes it challenging to predict how identified states influence human performance outcomes of relevance to military operations and other high-stakes domains. We describe a computational modeling approach to address this challenge, seeking to translate user states obtained from a variety of sources including wearable devices into relevant and actionable insights across the cognitive and physical domains. Three status predictors were considered: stress level, sleep status, and extent of physical exertion; these independent variables were used to predict three human performance outcomes: reaction time, executive function, and perceptuo-motor control. The approach provides a complete, conditional probabilistic model of the performance variables given the status predictors. Construction of the model leverages diverse raw data sources to estimate marginal probability density functions for each of six independent and dependent variables of interest using parametric modeling and maximum likelihood estimation. The joint distributions among variables were optimized using an adaptive LASSO approach based on the strength and directionality of conditional relationships (effect sizes) derived from meta-analyses of extant research. The model optimization process converged on solutions that maintain the integrity of the original marginal distributions and the directionality and robustness of conditional relationships. The modeling framework described provides a flexible and extensible solution for human performance prediction, affording efficient expansion with additional independent and dependent variables of interest, ingestion of new raw data, and extension to two- and three-way interactions among independent variables. Continuing work includes model expansion to multiple independent and dependent variables, real-time model stimulation by wearable devices, individualized and small-group prediction, and laboratory and field validation.

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Sleep Posture Recognition With a Dual-Frequency Cardiopulmonary Doppler Radar

Cited by 34 publications

References 47 publications

Radar-based remote physiological sensing: Progress, challenges, and opportunities

Radar-based remote physiological sensing: Progress, challenges, and opportunities

Noncontact Sleeping Heartrate Monitoring Method Using Continuous-Wave Doppler Radar Based on the Difference Quadratic Sum Demodulation and Search Algorithm

Toward Predicting Human Performance Outcomes From Wearable Technologies: A Computational Modeling Approach

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