Noncontact Infant Apnea Detection for Hypoxia Prevention With a K-Band Biomedical Radar.

Annually, a significant number of premature infants suffer from apnea, which can easily cause a drop in oxygen saturation levels, leading to hypoxia. However, infant cardiopulmonary monitoring using conventional methods often necessitates skin contact, and they are not suitable for long-term monitoring. This article introduces a non-contact technique for infant cardiopulmonary monitoring and an adjustable apnea detection algorithm. These are developed using a custom-designed K-band continuous-wave biomedical radar sensor system, which features a DC-coupled adaptive digital tuning function. By using radar technology to detect chest motions without physical contact, it is feasible to extract significant biological information regarding an infant's respiration and heartbeat. The proposed algorithm utilizes an adaptively adjusted threshold and personalized apnea warning time to automatically measure the total number of apneic events and their respective durations. Experiments have been conducted in clinical environment, demonstrating that both the accurate cardiopulmonary signals and the apneas of varying durations can be effectively monitored using this method, which suggest that the proposed technique has potential applications both inside and outside of clinical settings.

Accurate Measurement of Human Vital Signs With Linear FMCW Radars Under Proximity Stationary Clutters.

Vital sign detection using linear frequency-modulated continuous-wave (LFMCW) radar may be subject to the proximity stationary clutters. This paper presents a novel technique to synthesize the slow-time I/Q signals, which are equivalent to those in a single tone quadrature CW radar, from a single-channel LFMCW radar. It correlates the two types of radars in such a way that the proximity stationary clutters are translated to direct current (DC) offsets in the synthesized I/Q signals across slow-time. The circle-fitting based DC offsets calibration (DCcal) technique, which was developed for CW radar, can now be applied to eliminate the impact of the proximity stationary clutters in LFMCW radars for accurate vital sign detection. Moreover, the modified differentiate and cross-multiply (MDACM) algorithm can also be leveraged to eliminate the phase ambiguity issue. Thorough theoretical analysis and working principles are presented. Simulations are performed to validate the proposed technique. Moreover, exhaustive experiments are carried out with a millimeter-wave 79 GHz FMCW radar in the office environment. Mechanical vibration and vital signs are extracted with micrometer-level accuracy in the existence of proximity stationary clutters.

Millimeter-Wave Bat for Mapping and Quantifying Micromotions in Full Field of View.

Echolocating bats possess remarkable capability of multitarget spatial localization and micromotion sensing in a full field of view (FFOV) even in cluttered environments. Artificial technologies with such capability are highly desirable for various fields. However, current techniques such as visual sensing and laser scanning suffer from numerous fundamental problems. Here, we develop a bioinspired concept of millimeter-wave (mmWave) full-field micromotion sensing, creating a unique mmWave Bat ("mmWBat"), which can map and quantify tiny motions spanning macroscopic to µm length scales of full-field targets simultaneously and accurately. In mmWBat, we show that the micromotions can be measured via the interferometric phase evolution tracking from range-angle joint dimension, integrating with full-field localization and tricky clutter elimination. With our approach, we demonstrate the capacity to solve challenges in three disparate applications: multiperson vital sign monitoring, full-field mechanical vibration measurement, and multiple sound source localization and reconstruction (radiofrequency microphone). Our work could potentially revolutionize full-field micromotion monitoring in a wide spectrum of applications, while may inspiring novel biomimetic wireless sensing systems.

SleepSense: A Noncontact and Cost-Effective Sleep Monitoring System.

Quality of sleep is an important indicator of health and well being. Recent developments in the field of in-home sleep monitoring have the potential to enhance a person's sleeping experience and contribute to an overall sense of well being. Existing in-home sleep monitoring devices either fail to provide adequate sleep information or are obtrusive to use. To overcome these obstacles, a noncontact and cost-effective sleep monitoring system, named SleepSense, is proposed for continuous recognition of the sleep status, including on-bed movement, bed exit, and breathing section. SleepSense consists of three parts: a Doppler radar-based sensor, a robust automated radar demodulation module, and a sleep status recognition framework. Herein, several time-domain and frequency-domain features are extracted for the sleep recognition framework. A prototype of SleepSense is presented and evaluated using two sets of experiments. In the short-term controlled experiment, the SleepSense achieves an overall 95.1% accuracy rate in identifying various sleep status. In the 75-minute sleep study, SleepSense demonstrates wide usability in real life. The error rate for breathing rate extraction in this study is only 6.65%. These experimental results indicate that SleepSense is an effective and promising solution for in-home sleep monitoring.

Short-Range Noncontact Sensors for Healthcare and Other Emerging Applications: A Review.

Short-range noncontact sensors are capable of remotely detecting the precise movements of the subjects or wirelessly estimating the distance from the sensor to the subject. They find wide applications in our day lives such as noncontact vital sign detection of heart beat and respiration, sleep monitoring, occupancy sensing, and gesture sensing. In recent years, short-range noncontact sensors are attracting more and more efforts from both academia and industry due to their vast applications. Compared to other radar architectures such as pulse radar and frequency-modulated continuous-wave (FMCW) radar, Doppler radar is gaining more popularity in terms of system integration and low-power operation. This paper reviews the recent technical advances in Doppler radars for healthcare applications, including system hardware improvement, digital signal processing, and chip integration. This paper also discusses the hybrid FMCW-interferometry radars and the emerging applications and the future trends.

A Self-Calibrating Radar Sensor System for Measuring Vital Signs.

Vital signs (i.e., heartbeat and respiration) are crucial physiological signals that are useful in numerous medical applications. The process of measuring these signals should be simple, reliable, and comfortable for patients. In this paper, a noncontact self-calibrating vital signs monitoring system based on the Doppler radar is presented. The system hardware and software were designed with a four-tiered layer structure. To enable accurate vital signs measurement, baseband signals in the radar sensor were modeled and a framework for signal demodulation was proposed. Specifically, a signal model identification method was formulated into a quadratically constrained l1 minimization problem and solved using the upper bound and linear matrix inequality (LMI) relaxations. The performance of the proposed system was comprehensively evaluated using three experimental sets, and the results indicated that this system can be used to effectively measure human vital signs.

Assessment of human respiration patterns via noncontact sensing using Doppler multi-radar system.

Human respiratory patterns at chest and abdomen are associated with both physical and emotional states. Accurate measurement of the respiratory patterns provides an approach to assess and analyze the physical and emotional states of the subject persons. Not many research efforts have been made to wirelessly assess different respiration patterns, largely due to the inaccuracy of the conventional continuous-wave radar sensor to track the original signal pattern of slow respiratory movements. This paper presents the accurate assessment of different respiratory patterns based on noncontact Doppler radar sensing. This paper evaluates the feasibility of accurately monitoring different human respiration patterns via noncontact radar sensing. A 2.4 GHz DC coupled multi-radar system was used for accurate measurement of the complete respiration patterns without any signal distortion. Experiments were carried out in the lab environment to measure the different respiration patterns when the subject person performed natural breathing, chest breathing and diaphragmatic breathing. The experimental results showed that accurate assessment of different respiration patterns is feasible using the proposed noncontact radar sensing technique.

Accurate respiration measurement using DC-coupled continuous-wave radar sensor for motion-adaptive cancer radiotherapy.

Accurate respiration measurement is crucial in motion-adaptive cancer radiotherapy. Conventional methods for respiration measurement are undesirable because they are either invasive to the patient or do not have sufficient accuracy. In addition, measurement of external respiration signal based on conventional approaches requires close patient contact to the physical device which often causes patient discomfort and undesirable motion during radiation dose delivery. In this paper, a dc-coupled continuous-wave radar sensor was presented to provide a noncontact and noninvasive approach for respiration measurement. The radar sensor was designed with dc-coupled adaptive tuning architectures that include RF coarse-tuning and baseband fine-tuning, which allows the radar sensor to precisely measure movement with stationary moment and always work with the maximum dynamic range. The accuracy of respiration measurement with the proposed radar sensor was experimentally evaluated using a physical phantom, human subject, and moving plate in a radiotherapy environment. It was shown that respiration measurement with radar sensor while the radiation beam is on is feasible and the measurement has a submillimeter accuracy when compared with a commercial respiration monitoring system which requires patient contact. The proposed radar sensor provides accurate, noninvasive, and noncontact respiration measurement and therefore has a great potential in motion-adaptive radiotherapy.

A multi-radar wireless system for respiratory gating and accurate tumor tracking in lung cancer radiotherapy.

Respiratory gating and tumor tracking are two promising motion-adaptive lung cancer treatments, minimizing incidence and severity of normal tissues and precisely delivering radiation dose to the tumor. Accurate respiration measurement is important in respiratory-gated radiotherapy. Conventional gating techniques are either invasive to the body or bring insufficient accuracy and discomfort to the patients. In this paper, we present an accurate noncontact means of measuring respiration for the use in gated lung cancer radiotherapy. We also present an accurate tumor tracking technique for dynamical beam tracking radiotherapy. Two 2.4 GHz miniature radars were used to monitor the chest wall and abdominal movements simultaneously to get high resolution and enhanced parameter identification. Ray tracing technique was used to investigate the impact of antenna size in clinical practice. It is shown that our multiple radar system can reliably measure respiration signals for respiratory gating and accurate tumor tracking in motion-adaptive lung cancer radiotherapy.