Exploring Multiple Antennas for Long-range WiFi Sensing

Despite extensive research effort on contactless WiFi sensing over the past few years, there are still significant barriers hindering its wide application. One key issue is the limited sensing range due to the intrinsic nature of employing the weak target-reflected signal for sensing and therefore the sensing range is much smaller than the communication range. In this work, we address this challenging issue, moving WiFi sensing one step closer to real-world adoption. The key idea is to effectively utilize the multiple antennas widely available on commodity WiFi access points to simultaneously strengthen the target-reflected signal and reduce the noise. Although traditional beamforming schemes can help increase the signal strength, they are designed for communication and can not be directly applied to benefit sensing. To effectively increase the WiFi sensing range using multiple antennas, we first propose a new metric that quantifies the signal sensing capability. We then propose novel signal processing methods, which lay the theoretical foundation to support beamforming-based long-range WiFi sensing. To validate the proposed idea, we develop two sensing applications: fine-grained human respiration monitoring and coarse-grained human walking tracking. Extensive experiments show that: (i) the human respiration sensing range is significantly increased from the state-of-the-art 6-8 m to 11 m;1 and (ii) human walking can be accurately tracked even when the target is 18 m away from the WiFi transceivers, outperforming the sensing range of the state-of-the-art by 50%.

Analysis of a Dynamic Calibration Target for Through-Wall and Through-Rubble Motion Sensing Doppler Radar

Through-wall and through-barrier motion-sensing systems are becoming increasingly important tools to locate humans concealed behind barriers and under rubble. The sensing performance of these systems is best determined with appropriately designed calibration targets, which are ones that can emulate human motion. The effectiveness of various dynamic calibration targets that emulate human respiration, heart rate, and other body motions were analyzed. Moreover, these targets should be amenable to field deployment and not manifest angular or orientation dependences. The three targets examined in this thesis possess spherical polyhedral geometries. Spherical geometries were selected due to their isotropic radar cross-sectional characteristics, which provide for consistent radar returns independent of the orientation of the radar transceiver relative to the test target. The aspect-independence of a sphere allows for more accurate and repeatable calibration of a radar than using a nonspherical calibration artifact. In addition, the radar cross section (RCS) for scattering spheres is well known and can be calculated using far-field approximations. For Doppler radar testing, it is desired to apply these calibration advantages to a dynamically expanding-and-contracting sphere-like device that can emulate motions of the human body. Monostatic RCS simulations at 3.6 GHz were documented for each geometry. The results provide a visual way of representing the effectiveness of each design as a dynamic calibration target for human detection purposes.

Polymeric piezoresistive airflow sensor to monitor respiratory patterns

Monitoring human respiratory patterns is of great importance as it gives essential information for various medical conditions, e.g. sleep apnoea syndrome and chronic obstructive pulmonary disease and asthma, etc. Herein, we have developed a polymeric airflow sensor based on nanocomposites of vertically grown graphene nanosheets (VGNs) with polydimethylsiloxane (PDMS) and explored their applications in monitoring human respiration. The sensing performance of the VGNs/PDMS nanocomposite was characterized by exposing to a range of airflow rates (20–130 l min −1 ), and a linear performance with high sensitivity and low response time (mostly below 1 s) was observed. To evaluate the experimental results, finite-element simulation models were developed in the COMSOL Multiphysics package. The piezoresistive properties of VGNs/PDMS thin film and fluid–solid interaction were thoroughly studied. Laser Doppler vibrometry measures of sensor tip displacement closely approximated simulated deflection results and validated the dynamic response of the sensor. By comparing the proposed sensor and some other airflow sensors in the literature, it is concluded that the VGNs/PDMS airflow sensor has excellent features in terms of sensor height, detection range and sensitivity. The potential application of the VGNs/PDMS airflow sensor in detecting the respiration pattern of human exercises like walking, jogging and running has been demonstrated.

A processual view on the use of problem-based learning in high school physiology teaching

In problem-based learning (PBL), the steps and processes present in the PBL tutorial cycle are essential for constructive, self-directed, collaborative, and contextual student learning. This article presents a procedural study of a PBL tutorial cycle with high school students new to the method regarding human respiration and circulation physiology. We observed group dynamics and the learning process that occurred throughout the PBL tutorial cycle. The results indicate that conceptual changes were close to the planned learning objectives and that students enjoyed studying applying PBL. Moreover, a positive correlation was observed between group dynamics, self-directed learning and learning outcomes. Our results provide grounds for restructuring the tutorial cycle, especially important for novice PBL students, such as problem reformulation and the development and diversification of applied learning scaffolds. We conclude that the qualitative analysis performed herein can yield a deeper understanding of the PBL tutorial cycle and may be used to foster PBL implementation in institutions with little experience with the method and monitor its outcomes in organizations with mature PBL use.

An ultrafast-response and flexible humidity sensor for human respiration monitoring and noncontact safety warning

The humidity sensor is an essential sensing node in medical diagnosis and industrial processing control. To date, most of the reported relative humidity sensors have a long response time of several seconds or even hundreds of seconds, which would limit their real application for certain critical areas with fast-varying signals. In this paper, we propose a flexible and low-cost humidity sensor using vertically aligned carbon nanotubes (VACNTs) as electrodes, a PDMS-Parylene C double layer as the flexible substrate, and graphene oxide as the sensing material. The humidity sensor has an ultrafast response of ~20 ms, which is more than two orders faster than most of the previously reported flexible humidity sensors. Moreover, the sensor has a high sensitivity (16.7 pF/% RH), low hysteresis (<0.44%), high repeatability (2.7%), good long-term stability, and outstanding flexibility. Benefiting from these advantages, especially the fast response, the device has been demonstrated in precise human respiration monitoring (fast breathing, normal breathing, deep breathing, asthma, choking, and apnea), noncontact electrical safety warning for bare hand and wet gloves, and noncontact pipe leakage detection. In addition, the facile fabrication of the flexible platform with the PDMS-Parylene C double layer can be easily integrated with multisensing functions such as pH sensing, ammonium ion sensing, and temperature sensing, all of which are useful for more pattern recognition of human activity.

Pushing the Limits of Long Range Wireless Sensing with LoRa

Wireless sensing is an exciting new research area which enables a large variety of applications ranging from coarse-grained daily activity recognition to fine-grained vital sign monitoring. While promising in many aspects, one critical issue is the limited sensing range because weak reflection signals are used for sensing. Recently, LoRa signals are exploited for wireless sensing, moving a big step towards long-range sensing. Although promising, there is still a huge room for improvement. In this work, we qualitatively characterize the relationship between target movements and target-induced signal variations, and propose signal processing methods to enlarge the induced signal variation to achieve a longer sensing range. Experiment results show that the proposed system (1) pushes the contact-free sensing range of human walking from the state-of-the-art 50 m to 120 m; (2) achieves a sensing range of 75 m for fine-grained respiration sensing; and (3) demonstrates human respiration sensing even through seven concrete walls.

A Method for Reducing Timing Jitter’s Impact in Through-Wall Human Detection by Ultra-Wideband Impulse Radar

Ultra-wideband (UWB) impulse radar is widely used for through-wall human respiration detection due to its high range resolution and high penetration capability. UWB impulse radar emits very narrow time pulses, which can directly obtain the impulse response of the target. However, the time interval between successive pulses emitted is not ideally fixed because of timing jitter. This results in the impulse response position of the same target not being fixed, but it is related to slow-time. The clutter scattered by the stationary target becomes non-stationary clutter, which affects the accurate extraction of the human respiration signal. In this paper, we propose a method for reducing timing jitter’s impact in through-wall human detection by UWB impulse radar. After the received signal is processed by the Fast Fourier transform (FFT) in slow-time, we model the range-frequency matrix in the frequency domain as a superposition of the low-rank representation of jitter-induced clutter data and the sparse representation of human respiratory data. By only extracting the sparse component, the impact of timing jitter in human respiration detection can be reduced. Both numerical simulated data and experimental data demonstrate that our proposed method can effectively remove non-stationary clutter induced by timing jitter and improve the accuracy of the human target signal extraction.

Portable Micro-Doppler Radar with Quadrature Radar Architecture for Non-Contact Human Breath Detection

Recently, rapid advances in radio detection and ranging (radar) technology applications have been implemented in various fields. In particular, micro-Doppler radar has been widely developed to perform certain tasks, such as detection of buried victims in natural disaster, drone system detection, and classification of humans and animals. Further, micro-Doppler radar can also be implemented in medical applications for remote monitoring and examination. This paper proposes a human respiration rate detection system using micro-Doppler radar with quadrature architecture in the industrial, scientific, and medical (ISM) frequency of 5.8 GHz. We use a mathematical model of human breathing to further explore any insights into signal processes in the radar. The experimental system is designed using the USRP B200 mini-module as the main component of the radar and the Vivaldi antennas working at 5.8 GHz. The radar system is integrated directly with the GNU Radio Companion software as the processing part. Using a frequency of 5.8 GHz and USRP output power of 0.33 mW, our proposed method was able to detect the respiration rate at a distance of 2 m or less with acceptable error. In addition, the radar system could differentiate different frequency rates for different targets, demonstrating that it is highly sensitive. We also emphasize that the designed radar system can be used as a portable device which offers flexibility to be used anytime and anywhere.