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  1. Abstract Cognitive buildings use data on how occupants respond to the built environment to proactively make occupant-centric adjustments to lighting, temperature, ventilation, and other environmental parameters. However, sensors that unobtrusively and ubiquitously measure occupant responses are lacking. Here we show that Doppler-radar based sensors, which can sense small physiological motions, provide accurate occupancy detection and estimation of vital signs in challenging, realistic circumstances. Occupancy was differentiated from an empty room over 93% of the time in a 3.4 m × 8.5 m conference room with a single sensor in both wall and ceiling-mounted configurations. Occupancy was successfully detected while an occupant was under the table, visibly blocked from the sensor, a scenario where infrared, ultrasound, and video-based occupancy sensors would fail. Heart and respiratory rates were detected in all seats in the conference room with a single ceiling-mounted sensor. The occupancy sensor can be used to control HVAC and lighting with a short, 1–2 min delay and to provide information for space utilization optimization. Heart and respiratory rate sensing could provide additional feedback to future human-building interactive systems that use vital signs to determine how occupant comfort and wellness is changing with time. 
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    Free, publicly-accessible full text available December 1, 2024
  2. Respiration rate and heart rate variability (HRV) due to respiratory sinus arrhythmia (RSA) are physiological measurements that can offer useful diagnostics for a variety of medical conditions. This study uses a wrist-worn wearable development platform from Maxim Integrated and Doppler radar sensor developed by Adnoviv, Inc. to non-invasively measure these physiological signals. Six datasets are recorded comprising of five different individuals in varying physical environments breathing at different respiration rates. First, respiration rates are extracted from photoplethysmography (PPG) and accelerometer data and compared to Doppler radar. The average maximum and minimum difference between Doppler radar extracted RR and PPG, HRV RSA, and accelerometer extracted RR is 0.342 b/m and 0.171 b/m, respectively. Then, waveforms for Doppler radar, PPG, and HRV RSA signals are plotted in time domain and an analysis discusses the physical phenomena associated with the phase alignment of the signals. 
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  3. Identity authentication based on Doppler radar respiration sensing is gaining attention as it requires neither contact nor line of sight and does not give rise to privacy concerns associated with video imaging. Prior research demonstrating the recognition of individuals has been limited to isolated single subject scenarios. When two equidistant subjects are present, identification is more challenging due to the interference of respiration motion patterns in the reflected radar signal. In this research, respiratory signature separation techniques are functionally combined with machine learning (ML) classifiers for reliable subject identity authentication. An improved version of the dynamic segmentation algorithm (peak search and triangulation) was proposed, which can extract distinguishable airflow profile-related features (exhale area, inhale area, inhale/exhale speed, and breathing depth) for medium-scale experiments of 20 different participants to examine the feasibility of extraction of an individual’s respiratory features from a combined mixture of motions for subjects. Independent component analysis with the joint approximation of diagonalization of eigenmatrices (ICA-JADE) algorithm was employed to isolate individual respiratory signatures from combined mixtures of breathing patterns. The extracted hyperfeature sets were then evaluated by integrating two different popular ML classifiers, k-nearest neighbor (KNN) and support vector machine (SVM), for subject authentication. Accuracies of 97.5% for two-subject experiments and 98.33% for single-subject experiments were achieved, which supersedes the performance of prior reported methods. The proposed identity authentication approach has several potential applications, including security/surveillance, the Internet-of- Things (IoT) applications, virtual reality, and health monitoring. 
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  4. A number of algorithms have been developed to extract heart rate from physiological motion data using Doppler radar system. Yet, it is very challenging to eliminate noise associated with surroundings, especially with a single-channel Doppler radar system. However, single-channel Doppler radars provide the advantage of operating at lower power. Additionally, heart rate extraction using single-channel Doppler radar has remained somewhat unexplored. This has motivated the development of effective signal processing algorithms for signals received from single-channel Doppler radars. Three algorithms have been studied for estimating heart rate. The first algorithm is based on applying FFT on an FIR filtered signal. In the second algorithm, autocorrelation was performed on the filtered data. Thirdly, a peak finding algorithm was used in conjunction with a moving average preceded by a clipper to determine the heart rate. The results obtained were compared with heart rate readings from a pulse oximeter. With a mean difference of 2.6 bpm, the heart rate from Doppler radar matched that from the pulse oximeter most frequently when the peak finding algorithm was used. The results obtained using autocorrelation and peak finding algorithm (with standard deviations of 2.6 bpm and 4.0 bpm) suggest that a single channel Doppler radar system can be a viable alternative to contact heart rate monitors in patients for whom contact measurements are not feasible. 
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  5. Measurement of the body's displacement at multiple positions allows heart pulse wave propagation to be observed; this is an important step toward noncontact blood pressure measurement. This study investigates the feasibility of performing blood pressure measurements using skin displacement waveforms measured at two positions on a human body. To evaluate the accuracy of the proposed approach, this study uses a pair of laser displacement sensors to enable precise pulse transit time measurement. By comparing the displacement waveforms from the two sensors, the relationship between pulse transit time and blood pressure was evaluated. It is demonstrated experimentally that the blood pressure can be estimated with accuracy of 5.1 mmHg, which is equivalent to the error of an ordinary cuff-type blood pressure monitor. 
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  6. Abstract

    Human-Building Interaction (HBI) is a convergent field that represents the growing complexities of the dynamic interplay between human experience and intelligence within built environments. This paper provides core definitions, research dimensions, and an overall vision for the future of HBI as developed through consensus among 25 interdisciplinary experts in a series of facilitated workshops. Three primary areas contribute to and require attention in HBI research: humans (human experiences, performance, and well-being), buildings (building design and operations), and technologies (sensing, inference, and awareness). Three critical interdisciplinary research domains intersect these areas: control systems and decision making, trust and collaboration, and modeling and simulation. Finally, at the core, it is vital for HBI research to center on and support equity, privacy, and sustainability. Compelling research questions are posed for each primary area, research domain, and core principle. State-of-the-art methods used in HBI studies are discussed, and examples of original research are offered to illustrate opportunities for the advancement of HBI research.

     
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  7. null (Ed.)
    Effective radar cross-section (ERCS) for microwave Doppler radar, is defined by the reflected power from sections of the human body that undergo physiological motion. This paper investigates ERCS for human cardiopulmonary motion of sedentary subjects at three different positions (front, back and side with respect to radar). While human breathing and heartbeat can be measured from all four sides of the body, the characteristics of measured signals will vary with body orientation. Thus, continuous wave radar with quadrature architecture at 2. 4GHz was used to test a sedentary subject for three minutes from three different orientations: front, back and side with respect to radar. The results obtained from the tests showed that physiological motion could be obtained and that distinct patterns emerge due to the differences in the ERCS for each orientation. For the seated subject, back ERCS was higher than for front and side positions. Determining ERCS changes with position may enable determining body orientation with respect to the radar. This research opens further opportunities for development of high-resolution occupancy sensing and emergency search and rescue sensing, where the orientation of a human subject may be unknown ahead of time. 
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