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A 28-GHz multibeam joint communication and sensing system called SideSense is presented, in which a line-of-sight (LoS) beam is used to maintain reliable communication, while other sensing beams are used to enhance physiological motion detection. SideSense decodes the motion frequency and shape from the channel state information (CSI) by first tuning the gain ratio and phase differences between the LoS communication beam and non-LoS (NLoS) beam to maximize the sensing signal-to-noise ratio (SSNR) without significantly degrading the communication channel capacity (CCC). Analytical results based on a bistatic model are presented to show a gain ratio of around 1 and a phase difference of 90° or 270°, which are ideal for optimizing both SSNR and CCC. Experiments based on an array of phased array (APA) beamformers and orthogonal frequency-division multiplexing (OFDM) waveforms with phantom and human subjects are presented to validate the performance of SideSense. Results show that SideSense can improve SSNR by 84% while reducing CCC by 35%, an acceptable decrease within the normal operational parameters of a millimeter-wave (mmWave) communication system, which would not trigger a link reestablishment procedure, e.g., communication beam realignment. 

This paper proposes a spectral binning method for the classification of locomotion and extraneous body motion (EBM) that may occur during Continuous Wave (CW) Doppler radar motion sensing of human subjects. The method analyzes the spectral content of the arctangent demodulated displacement signature, generating an activity classification based on the magnitude of the spectral content for each of several frequency bins. The choice and number of bins used for the overall classification of data were determined by analyzing experimental data. The method successfully classified sedentary, EBM, and locomotion states for 5 subjects. The method can be used both for determining the presence and type of activity, and for recognizing when data segments are not suitable for monitoring sedentary vital signs. 

The significant impact of environmental noise on cardiovascular health is becoming more widely recognized. However, detailed studies related to environmental noise and human physiology are very limited. This study provides a detailed evaluation of heart rate and respiratory responses to different noise levels in a controlled experimental environment using Doppler radar. The findings indicate that while respiratory rates show notable variations, heart rates remain largely unchanged with alterations in noise levels. This non-invasive approach to assessing the effects of environmental noise using Doppler radar could help improve indoor environmental quality by adapting conditions to enhance acoustic comfort and reduce stress for occupants. 

A static algorithm-based method is described here to differentiate between recoverable sedentary respiratory rate data extraneous motion segments measured using Doppler radar. Extraneous motion such as locomotion and fidgeting can cause drastic changes in dc offset and SNR of the received signal. Such extraneous data may not be excluded and can lead to an erroneous assessment of the respiration rate. In some cases, however, moderate distinct extraneous motion does not completely occlude the measurement of respiratory torso motion, allowing for respiration rate recovery. This work focuses on the accurate classification of data which is suitable for respiration rate analysis in the presence of locomotion and small extraneous movements. The proposed algorithm has been demonstrated to be accurate for classifying data with recoverable respiratory rates for 2 subjects and 3 types of fidgets with 99.4% accuracy on average. 

Heart rate variability (HRV) analysis using Doppler radar (DR) is a promising method for noninvasive health and stress assessment. However, the respiration signal harmonic content typically limits HRV parameter estimation accuracy. While several harmonic reduction techniques have been used to improve the average HR estimation accuracy, achieving high beat-to-beat interval (BBI) accuracy is still challenging. This article demonstrates that arctangent demodulation (AD) with wavelet-based signal processing enhanced with template matching is effective to estimate HRV parameters with accuracy on the order of 10 ms with 2.4 GHz DR. Moreover, it was theoretically and experimentally demonstrated that the cases where AD provides limited improvement due to phase delay between thorax and abdomen motion are easily identifiable, and can be alternatively processed using a single-channel data. 

An overnight sleep study can provide vital health diagnostics yet typically involves applying and monitoring multiple body-contact sensors, which can interfere with sleep and require cumbersome manual data analysis. Doppler radar technology has been demonstrated to provide a non-invasive means of measuring vital signs through clothing and bedding, including respiratory rate, heart rate, motion activity, body position, and tidal respiratory volume. This paper examines the potential for applying physiological radar to assess sleep apnea and intervention strategies. 

Accurate continuous measurement of respiratory displacement using continuous wave Doppler radar requires rigorous management of dc offset which changes when a subject changes distance from the radar measurement system. Effective measurement, therefore, requires robust dynamic calibration which can recognize and compensate for changes in the nominal position of a subject. In this paper, a respiratory displacement measurement algorithm is proposed which can differentiate between sedentary and non-sedentary conditions and continuously adapt to provide long-term monitoring of a subject’s sedentary respiration. Arctangent demodulation is an effective means of quantifying continuous displacement using a quadrature Doppler radar, yet it depends on accurate identification of dc offset and dc information contributions in the radar I-Q arc with the subject in a particular position. The dynamic calibration method proposed here is demonstrated to differentiate between sedentary and non-sedentary conditions for six subjects to produce accurate sedentary respiration measurements even when the subject arbitrarily changes position, once the appropriate thresholds are established for the measurement environment.