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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.

 

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. 

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. 

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. 

In-home sleep monitoring system using Microwave Doppler radar is gaining attention as it is unobtrusive and noncontact form of measurement. Most of the reported results in literature focused on utilizing radar-reflected signal amplitude to recognize Obstructive sleep apnea (OSA) events which requires iterative analysis and cannot recommend about sleep positions also (supine, prone and side). In this paper, we propose a new, robust and automated ERCS-based (Effective Radar Cross section) method for classifying OSA events (normal, apnea and hypopnea) by integrating radar system in a clinical setup. In our prior attempt, ERCS has been proven versatile method to recognize different sleep postures. We also employed two different machine learning classifiers (K-nearest neighbor (KNN) and Support Vector machine (SVM) to recognize OSA events from radar captured ERCS and breathing rate measurement from five different patients' clinical study. SVM with quadratic kernel outperformed with other classifiers with an accuracy of 96.7 % for recognizing different OSA events. The proposed system has several potential applications in healthcare, continuous monitoring and security/surveillance applications. 

While Doppler radar measurement of respiration has shown promise for various healthcare applications, simultaneous sensing of respiration for multiple subjects in the radar field of view remains a significant challenge as reflections from the subjects are received as an interference pattern. Prior research has demonstrated the basic feasibility of using phase comparison with a 24-GHz Monopulse radar for isolation of one subject when another subject was in view, by estimating each subject's angular location with 88% accuracy. The integration of the high-resolution Multiple Signal Classification (MUSIC) algorithm with a phase-comparison technique is proposed to achieve robust accuracy for practical multi-subject respiration monitoring. Experimental results for this work demonstrate that the MUSIC pseudo-spectrum can separate two subjects 1.5 meters apart from each other at a distance of 3 meters from the sensor, using the same antenna array elements, spacing, and experimental scenarios previously reported for phase comparison Monopulse alone. Experimental results demonstrate that the MUSIC algorithm outperforms the phase-comparison technique with an azimuth angular position estimation accuracy over 95%. Higher accuracy indicates the system has improved robustness concerning noise and interference. 

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. 

Non-contact home-based sleep monitoring will bring a paradigm shift to diagnosis and treatment of Obstructive Sleep Apnea (OSA) as it can facilitate easier access to specialized care in order to reach a much boarder set of patients. However, current remote unattended sleep studies are mostly contact sensor based and test results are sometimes falsified by sleep-critical job holders (driver, airline pilots) due to fear of potential job loss. In this work, we investigated identity authentication of patients with OSA symptoms based on extracting respiratory features (peak power spectral density, packing density and linear envelop error) from radar captured paradoxical breathing patterns in a small-scale clinical sleep study integrating three different machine learning classifiers (Support Vector Machine (SVM), K-nearest neighbor (KNN), Random forest). The proposed OSA-based authentication method was tested and validated for five OSA patients with 93.75% accuracy using KNN classifier which outperformed other classifiers.