Radio Analytics for Indoor Monitoring and Human Recognition

In the era of Internet of Things (IoT), researchers have been developing new technologies and intelligent systems to answer the question of who, what, when, where, and how of things happening in the environment. Among the various techniques that measure what is happening in the surroundings, wireless sensing stands out because of its ubiquity and prevalence. On one hand, different indoor activities bring distinctive perturbations to wireless radio propagation. On the other hand, thanks to the nature of multipath, indoor environmental information is recorded and embedded in the wireless channel state information (CSI). Hence, by deploying wireless transceivers to sense the radio propagation environment and analyzing the CSI, one can extend human senses and enrich her/his insight into surrounding environments and activities. By leveraging the natural multipath propagation of electromagnetic (EM) waves, radio analytics is proposed as a promising technology that deciphers radio propagation characteristics and reveals rich environmental information surrounding us. As one approach of radio analytics, time-reversal (TR) technique exploits the information of large degrees-of-freedom delivered by CSI and provides a high-resolution spatial-temporal resonance, by treating each multipath component in a wireless channel as a distributed virtual antenna. The TR spatial-temporal resonance is indeed a resonance of EM field in response to the propagation environment, and it changes whenever the propagation environment changes. Inspired by the principle of TR and motivated by the development of IoT, in this dissertation, we propose several radio analytic systems that leverage multipath information to realize IoT applications of recognizing different events and identifying people in an indoor environment. In the first part, we design three indoor monitoring systems that analyze different event-determined features extracted from either a single CSI sample or a CSI time series. The first proposed indoor monitoring system distinguishes between different indoor events by matching the instantaneous CSI to a multipath profile calibrated in a training database whose similarity is quantified by the time-reversal resonance strength (TRRS). Later on, we derive the statistics of TRRS, and we propose a new TR based indoor monitoring system that differentiates between different indoor events based on the statistical behavior of TRRS. Unlike the previous two indoor monitoring systems which treats each CSI as an independent feature, we propose the third indoor monitoring system by exploiting the temporal information embedded in the CSI time series as an additional feature to comprehensively understand indoor events. Results of extensive experiments demonstrate the proposed systems as promising solutions to future indoor monitoring IoT applications. In the second part of this dissertation, we propose the concept of human radio biometrics and design a through-the-wall human identification system that is implemented on commercial WiFi devices. As a human present in an indoor environment, the radio waves propagate around will interact with the human body through reflection and scattering. We define human radio biometrics as the attenuation and alteration of wireless signals brought by human. We achieve an accurate through-the-wall human recognition by utilizing the fact that the radio biometrics are uniquely determined by the biological characteristics of each human. Through extensive experiments, we validate the existence of radio biometrics and evaluate the accuracy of the proposed human identification system. Unlike conventional approaches for biometric recognition, the proposed radio biometrics system can identify human through a wall and supports commercial WiFi infrastructure, thus illustrating its potential for human recognition IoT applications

RADIO ANALYTICS FOR HUMAN ACTIVITY MONITORING AND INDOOR TRACKING

With the rapid development of the Internet of Things (IoT), wireless sensing has found wide applications from wellbeing monitoring, activity recognition, to indoor tracking. In this dissertation, we investigate the problem of wireless sensing for IoT applications using only ambient radio signals, e.g., WiFi, LTE, and 5G. In particular, our work mainly focuses on passive speed estimation, motion detection, sleep monitoring, and indoor tracking for wireless sensing. In this dissertation, we first study the problem of indoor speed estimation using WiFi channel state information (CSI). We develop the statistical electromagnetic (EM) wave theory for wireless sensing and establish a link between the autocorrelation function (ACF) of the physical layer CSI and the speed of a moving object. Based on the developed statistical EM wave theory for wireless sensing, we propose a universal low-complexity indoor speed estimation system leveraging CSI, which can work in both device-free and device-based situations. The proposed speed estimator differs from the other schemes requiring strong line-of-sight conditions between the source and observer in that it embraces the rich-scattering environment typical for indoors to facilitate highly accurate speed estimation. Moreover, as a calibration-free system, it saves the users' efforts from large-scale training and fine-tuning of system parameters. The proposed speed estimator can enable many IoT applications, e.g., gait monitoring, fall detection, and activity recognition. Then, we also study the problem of indoor motion detection using CSI. The statistical behaviors of the CSI dynamics when motion presents can be characterized by the developed statistical EM theory for wireless sensing. We formulate the motion detection problem as a hypothesis testing problem and also derive the relationship between the detection rate and false alarm rate for motion detection, which is independent of locations, environments and motion types. Thus, the proposed motion detection system can work in most indoor environments, without any scenario-tailored training efforts. Extensive experiments conducted in several facilities show that the proposed system can achieve better detection performance compared to the existing CSI-based motion detection systems while maintaining a much larger coverage and a much lower false alarm rate. This dissertation also focuses on sleep monitoring using CSI. First, we build a statistical model for maximizing the signal-to-noise (SNR) ratio of breathing signal, which accounts for all reflecting and scattering multipaths, allowing highly accurate and instantaneous breathing estimation with best-ever performance achieved on commodity devices. Our results demonstrate that the proposed breathing estimator yields a median absolute error of 0.47 bpm and a 95%-tile error of only 2.92 bpm for breathing estimation, and detects breathing robustly even when a person is 10m away from the WiFi link, or behind a wall. Then, we apply machine learning algorithms on the extracted features from the estimated breathing rates to classify different sleep stages, including wake, rapid eye movement (REM), and non-REM (NREM), which was previously only possible with dedicated hardware. Experimental results show that the proposed sleep monitoring system achieves sleep staging accuracy of 88%, outperforming advanced solutions using contact sensor or radar. The last work of this dissertation considers the problem of indoor tracking using CSI. First, we leverage a stationary and location-independent property of the time-reversal (TR) focusing effect of radio signals for highly accurate moving distance estimation, which plays a key role in the proposed indoor tracking system. Together with the direction estimation based on inertial measurement unit and location correction using the constraints from the floorplan, the proposed indoor tracking system is shown to be able to track a moving object with decimeter-level accuracy in different environments

Proceedings of the Second International Mobile Satellite Conference (IMSC 1990)

Presented here are the proceedings of the Second International Mobile Satellite Conference (IMSC), held June 17-20, 1990 in Ottawa, Canada. Topics covered include future mobile satellite communications concepts, aeronautical applications, modulation and coding, propagation and experimental systems, mobile terminal equipment, network architecture and control, regulatory and policy considerations, vehicle antennas, and speech compression