Physiological Parameter Sensing with Wearable Devices and Non-Contact Dopper Radar.

M.S. Thesis. University of Hawaiʻi at Mānoa 2017

Doppler Radar Techniques for Distinct Respiratory Pattern Recognition and Subject Identification.

Ph.D. Thesis. University of Hawaiʻi at Mānoa 2017

Rf sensing and processing methods for noninvasive health monitoring

Vulnerable populations include groups of people with a higher risk of poor health as a result of the limitations due to illness or disability. The health issues of vulnerable populations include three categories: physical, psychological, and social. The people with physical issues include high-risk mothers and infants, older adults and others with chronic illnesses and people with disabilities. The psychological issues of vulnerable populations include chronic mental conditions, such as bipolar disorder, major depression, and hyperactivity disorder, as well as substance abuse and those who are suicidal. The social issues in vulnerable populations include those living in abusive families, the homeless, etc. This dissertation concentrates on methods for helping two groups of vulnerable populations, namely, frail older adults and psychiatric hospital patients, to monitor their activity level, respiration rate, sleeping quality, and restless time in bed. In the first part of our work, we investigate a contactless monitoring system for psychiatric patients in a naturalistic hospital setting that can track their motion in bed, estimate the breathing rate of patients during their peaceful sleeping periods, and can be used to estimate a patient's restless time and sleep quality. Specifically, the contactless monitoring system uses a Vayyar Radar system with a carrier frequency of 6.014 GHz to capture all reflections by the FMCW (frequency modulation continuous waveform) signal. The Vayyar Radar system has been installed in a Psychiatric Center to capture 12 nights with over 135 hours of data from 7 patients. A depth camera and a thermal camera have also been installed and are used as the ground truth. The goal is to classify in bed and out of bed classes, quantify restlessness in bed, and determine the breathing rate while patients are lying in bed. We have simulated the psychiatric hospital set-up in the lab, where a respiration belt is used for ground truth, and tested the system with body postures of patients observed in the psychiatric hospital. We estimated respiration rate with different sleep postures, with the aim of investigating a contactless monitoring system for psychiatric patients in the hospital that can estimate the breathing rate of patients during typical sleeping postures, and find the torso area when the patients use other postures, such as reading books in bed or reversing the body on the bed. In the second part of our work, we investigate two methods for learning the room structure via radio wave reflections for longitudinal health monitoring of older adults in a naturalistic home setting. The goal is to use these data as part of a monitoring system that can be easily installed in a home with minimal configuration, for the purpose of detecting very early signs of illness and functional decline. Two studies are conducted using RF (radio frequency) sensing. The first method learns the structure from the RF clutter patterns and uses the beat frequency of the maximum peak in each chirp to calculate the wall position. The second method learns the room structure from active movement patterns and uses the open space between the clusters of active movement patterns to estimate the possible wall locations. Comparing the two results from these methods provides a more robust wall location. In addition, a background filter is designed based on the wall position, and the activity level of people in different rooms is estimated using a fuzzy rule system applied to the RF motion data

Wide Band Embedded Slot Antennas for Biomedical, Harsh Environment, and Rescue Applications

For many designers, embedded antenna design is a very challenging task when designing embedded systems. Designing Antennas to given set of specifications is typically tailored to efficiently radiate the energy to free space with a certain radiation pattern and operating frequency range, but its design becomes even harder when embedded in multi-layer environment, being conformal to a surface, or matched to a wide range of loads (environments). In an effort to clarify the design process, we took a closer look at the key considerations for designing an embedded antenna. The design could be geared towards wireless/mobile platforms, wearable antennas, or body area network. Our group at UT has been involved in developing portable and embedded systems for multi-band operation for cell phones or laptops. The design of these antennas addressed single band/narrowband to multiband/wideband operation and provided over 7 bands within the cellular bands (850 MHz to 2 GHz). Typically the challenge is: many applications require ultra wide band operation, or operate at low frequency. Low frequency operation is very challenging if size is a constraint, and there is a need for demonstrating positive antenna gain

RF Sensing Technologies for Assisted Daily Living in Healthcare: A Comprehensive Review

The aim of radio-frequency (RF) sensing for assisted living is to deliver automatic support and monitoring for older people in their homes, impaired patients living independently, individuals in need of continuous support, and people suffering from chronic diseases that require them to stay in care-homes or at hospitals. RF sensing technologies have the potential to improve the quality of living of elderly people or disabled individuals in need of timely assistance. This paper provides a comprehensive review on three of the most innovative RF sensing technologies for activities of daily living in healthcare sector (namely active radar, passive radar, and wireless channel information and RFID sensing) and presents some of the open challenges that need to be addressed

Telemedicine

Telemedicine is a rapidly evolving field as new technologies are implemented for example for the development of wireless sensors, quality data transmission. Using the Internet applications such as counseling, clinical consultation support and home care monitoring and management are more and more realized, which improves access to high level medical care in underserved areas. The 23 chapters of this book present manifold examples of telemedicine treating both theoretical and practical foundations and application scenarios

Behind-wall target detection using micro-doppler effects

Abstract: During the last decade technology for seeing through walls and through dense vegetation has interested many researchers. This technology offers excellent opportunities for military and police applications, though applications are not limited to the military and police; they go beyond those applications to where detecting a target behind an obstacle is needed. To be able to disclose the location and velocity of obscured targets, scientists’ resort to electromagnetic wave propagation. Thus, through-the-wall radar (TWR) is technology used to propagate electromagnetic waves towards a target through a wall. Though TWR is a promising technology, it has been reported that TWR imaging (TWRI) poses a range of ambiguities in target characterisation and detection. These ambiguities are related to the thickness and electric properties of walls. It has been reported that the mechanical and electric properties of the wall defocus the target image rendered by the radar. The defocusing problem is the phenomenon of displacing the target away from its true location when the image is rendered. Thus, the operator of the TWR will have a wrong position, not the real position of the target. Defocusing is not the only problem observed while the signal is travelling through the wall. Target classification, wall modelling and others are areas that need investigation...D.Ing. (Electrical and Electronic Engineering

Towards In-baggage Suspicious Object Detection Using Commodity WiFi

The growing needs of public safety urgently require scalable and low-cost techniques on detecting dangerous objects (e.g., lethal weapons, homemade-bombs, explosive chemicals) hidden in baggage. Traditional baggage check involves either high manpower for manual examinations or expensive and specialized instruments, such as X-ray and CT. As such, many public places (i.e., museums and schools) that lack of strict security check are exposed to high risk. In this work, we propose to utilize the fine-grained channel state information (CSI) from off-the-shelf WiFi to detect suspicious objects that are suspected to be dangerous (i.e., defined as any metal and liquid object) without penetrating into the user's privacy through physically opening the baggage. Our suspicious object detection system significantly reduces the deployment cost and is easy to set up in public venues. Towards this end, our system is realized by two major components: it first detects the existence of suspicious objects and identifies the dangerous material type based on the reconstructed CSI complex value (including both amplitude and phase information); it then determines the risk level of the object by examining the object's dimension (i.e., liquid volume and metal object's shape) based on the reconstructed CSI complex of the signals reflected by the object. Extensive experiments are conducted with 15 metal and liquid objects and 6 types of bags in a 6-month period. The results show that our system can detect over 95% suspicious objects in different types of bags and successfully identify 90% dangerous material types. In addition, our system can achieve the average errors of 16ml and 0.5cm when estimating the volume of liquid and shape (i.e., width and height) of metal objects, respectively

Near field sensing and antenna design for wireless body area network

PhD ThesisWireless body area network (WBAN) has emerged in recent years as a special class of wireless sensor network; hence, WBAN inherits the wireless sensor network challenges of interference by passive objects in indoor environments. However, attaching wireless nodes to a person’s body imposes a unique challenge, presented by continuous changes in the working environment, due to the normal activities of the monitored personnel. Basic activities, like sitting on a metallic chair or standing near a metallic door, drastically change the antenna behaviour when the metallic object is within the antenna near field. Although antenna coupling with the human body has been investigated by many recent studies, the coupling of the WBAN node antenna with other objects within the surrounding environment has not been thoroughly studied. To address the problems above, the thesis investigates the state-of-the art of WBAN, eximanes the influence of metallic object near an antenna through experimental studies and proposes antenna design and their applications for near field environments. This thesis philosophy for the previously mentioned challenge is to examine and improve the WBAN interaction with its surrounding by enabling the WBAN node to detect nearby objects based solely on change in antenna measurements. The thesis studies the interference caused by passive objects on WBAN node antenna and extracts relevant features to sense the object presence within the near field, and proposes new design of WBAN antenna suitable for this purpose. The major contributions of this study can be summarised as follows. First, it observes and defines the changes in the return loss of a narrow band antenna when a metallic object is introduced in its near field. Two methods were proposed to detect the object, based on the refelction coefficient and transmission coefficient of an antenna in free space. Then, the thesis introduces a new antenna design that conforms to the WBAN requirements of size, while achieving very low sensitivity to human body. This was achieved through combining two opposite Vivaldi shapes on one PCB and using a metallic sheet to act as a reflector, which minimised the antenna coupling with the human body and reduced the radiation pattern towards the body. Finally, the proposed antennas were tested on several human body parts with nearby metallic objects, to compare the change in antenna s-parameters due to presence of the human body and presence of the metallic object. Based on the measurements, basic statistical indicators and Principal Component Analysis were proposed to detect object presense and estimate its distance. In conclusion, the thesis successfully shows WBAN antenna’s ability to detect nearby metallic objects through a set of proposed indicators and novel antenna design. The thesis is wrapped up by the suggestion to investigate time domain features and modulated signal for future work in WBAN near field sensing

Design of software defined radio based testbed for smart healthcare

Human Activity Recognition (HAR) help to sense the environment of a human being with an objective to serve a diverse range of human-centric applications in health care, smart-homes and the military. The prevailing detection techniques use ambient sensors, cameras and wearable devices that primarily require strenuous deployment overheads and raise privacy concern as well. Monitoring human activities of daily living is a possible way of describing the functional and health status of a human. Therefore, human activity recognition (HAR) is one of genuine components in personalized life-care and healthcare systems, especially for the elderly and disabled. Recent advances in wireless technologies have demonstrated that a person’s activity can modulate the wireless signal, and enable the transfer of information from a human to an RF transceiver, even when the person does not carry a transmitter. The aim of this PhD project is to design a novel, non-invasive, easily deployable, flexible and scalable test-bed for detecting human daily activities that can help to assess the general physical health of a person based on Software Defined Radios (SDRs). The proposed system also allows us to modify the power level of transceiver model, change the operating frequency, use self-design antennas and change the number of subcarriers in real-time. The results obtained using USRP based wireless sensing for activities of daily living are highly accurate as compared to off-the-shelf wireless devices each time when activities and experiments are performed. This system leverage on the channel state information (CSI) to record the minute movement caused by breathing over orthogonal frequency division multiplexing (OFDM) in multiple sub-carriers. The proposed system combines subject count and activities performed in different classes together, resulting in simultaneous identification of occupancy count and activities performed. Different machine learning algorithms namely K-Nearest Neighbour, Decision Tree, Discriminant Analysis, and Naıve Bayes are used to evaluate the overall performance of the test-bed and achieved a high accuracy. The K-nearest neighbour outperformed all classifiers, providing an accuracy of 89.73% for activity detection and 91.01% for breathing monitoring. A deep learning convolutional neural network is engineered and trained on the CSI data to differentiate multi-subject activities. The proposed system can potentially fulfill the needs of future in-home health activity monitoring and is a viable alternative for monitoring public health and well being