Printed Spiral Coil Design, Implementation, And Optimization For 13.56 MHz Near-Field Wireless Resistive Analog Passive (WRAP) Sensors

Noroozi, Babak. Ph.D. The University of Memphis. June 2020. Printed Spiral Coil Design, Implementation, and Optimization for 13.56 MHz Near-Field Wireless Resistive Analog Passive (WRAP) Sensors. Major Professor: Dr. Bashir I. Morshed.Monitoring the bio-signals in the regular daily activities for a long time can embrace many benefits for the patients, caregivers, and healthcare system. Early diagnosis of diseases prior to the onset of serious symptoms gives more time to take some preventive action and to begin effective treatment with lower cost. These health and economy benefits are achievable with a user-friendly, low-cost, and unobtrusive wearable sensor that can easily be carried by a patient with no interference with the normal life. The easy application of such sensor brings the smart and connected community (SCC) idea to existence. The spread of a designated disease, like COVID-19, can be studied by collecting the physiological signals transmitted from the wearable sensors in conjunction with a mobile app interface. Moreover, such a comfortable wearable sensor can help to monitor the vital signals during fitness activities for workout concerns. The desire of such wearable sensor has been responded in many researches and commercial products such as smart watch and Fitbit. Wireless connection between the sensor on the body and the scanner is the key and common factor of all convenient wearables. This essential feature has been currently addressed by the costly techniques which is the main impediment to be widely applicable. The existing wireless methods including WiFi, Bluetooth, RFID, and NFC impose cost, complexity, weight, and extra maintenance including battery replacement or recharging, which drove us to propose a low-cost, convenient, and simple technique for wireless connection suitable for battery-less fully-passive sensors. Using a pair of coils connected by the near-field magnetic induction has been copiously used in wireless power transfer (WPT) for medical and industrial applications. However, near field RFID and NFC rely on this technique with active circuits. In contrast, we have proposed a wireless resistive analog passive (WRAP) sensor in which a resistive transducer at the secondary side, affects the primary quality factor (Q) through the inductive connection between a pair of square-shaped Printed Spiral Coils (PSC). The primary 13.56 MHz (ISM band) signal is modulated in response to the continuous change of bio-signal and the amount of response to the unit change in transducer resistance is defined as sensitivity. A higher sensitivity enables the system to respond to the smaller bio-signals and increases the coils maximum relative mobilities. The PSCs specifications and circuit components determine the sensitivity and its tolerance to the coils displacements. We first define and formulize the objective function for coil and components optimization to achieve the maximum sensitivity. Although the optimization methods do not show much different results, due to the speed and simplicity, the Genetic Algorithm (GA) technique is chosen as an advanced method. Then in second optimization stage, the axial and lateral distances that affect the mutual inductance are introduced to the optimization process. The results as a pair of PSCs profiles and the associated circuit components are obtained and fabricated that produced the maximum sensitivity and misalignment tolerance. For the sake of patient comfort, the secondary coil size is fixed at 20 mm and the primary coil is optimized at 60 mm with the maximum (normalized) sensitivity 1.3 m for 16 mm axial distance. If the Read-Zone is defined as the space in which the center of secondary coil can move and the sensitivity keeps at least half of its maximum value, the best Read-Zone has a conical shape with the base radius 22.5 mm and height 14 mm. The analytical results are verified by the measurement results on the fabricated coils and circuits

A HARDWARE-SOFTWARE CO-DESIGNED WEARABLE FOR REAL-TIME PHYSIOLOGICAL DATA COLLECTION AND SIGNAL QUALITY ASSESSMENT

In the future, Smart and Connected Communities (S&CC) will use distributed wireless sensors and embedded computing platforms to produce meaningful data that can help individuals, and communities. Here, we presented a scanner, a data reliability estimation algorithm and Electrocardiogram (ECG) beat classification algorithm which contributes to the S&CC framework .In part 1, we report the design, prototyping, and functional validation of a low-power, small, and portable signal acquisition device for these sensors. The scanner was fully tested, characterized, and validated in the lab, as well as through deployment to users homes. As a test case, we show results of the scanner measuring WRAP temperature sensors with relative error within the 0.01% range. The scanner measurement shows distinguish temperature of 1F difference and excellent linear dependence between actual and measured resistance (R2 = 0.998). This device hasdemonstrated the possibility of a small, low-power portable scanner for WRAP sensors.Additionally, we explored the statistical data reliability metric (DReM) to explain the quality of bio-signal quantitatively on a scale between 0.0 -1.0. As proof of concept, we analyzed the ECG signal. Our DReM prediction algorithm measures the reliability of the ECG signals effectively with low Root mean square error = 0.010 and Mean absolute error = 0.008 and coefficient of determination R2 value of 0.990. Finally, we tested our model against the opinions of three independent judges and presented R2 value to determine the agreement between judgments vs our prediction model.We concluded our contribution to the S&CC framework by analyzing ECG beat classification with a pipeline of classifiers that focuses on improving the models performance on identifying minority classes (ventricular ectopic beat, supraventricular ectopic beat). Moreover, we intended to minimize morphological distortion introduced due to indiscriminate use of filtering techniques on ECG signals. Our approach shows an average positive predictive value 95.21%, sensitivity of95.28%, and F-1 score 95.76% respectively

Framework For Spatiotemporal Visualization of Community Health In a Smart And Connected Community (SCC)

Smart and Connected Community (SCC) will use health data of the community members for knowledge generation beyond mobile health (mHealth). Current mHealth only assists individual users to monitor their health status, but do not allow integration and interpretation of collective health data. The objective of this thesis is to exhibit the continuous health status of the community members through a framework of visualization including spatial and temporal plots, such as anonymous user health severity graph, severity flow plot, a severity map view, the cumulative and segmented animation. The framework composes of physiological data collection with smartphones and sharing of anonymous data to SCC health server. Physiological data is sent from the smartphone app in JSON (JavaScript Object Notation) format and stored in the server database. Temporal visualization is presented as graph and flow, whereas spatial visualization utilizes Google Map overlay to display the severity distribution through the color code of severity. Furthermore, an animation mode is developed that displays combined spatiotemporal data over the selected duration in either cumulative or segmented at specified intervals. To implement this, a web-based dynamic server is used. The front end of the server is built with JavaScript JQuery and Ajax, whereas the backend of the server is managed by Hypertext Preprocessor, i.e. PHP, a server-side scripting language. The phpMyAdmin (administration tool for MySQL) stores the JSON data that comes from the smartphone app. To assess the framework, we utilized the MIT-BIH database with pre-recorded data from Arrhythmia patients. We assume each dataset record as a community member (subject). From these records, we classified arrhythmia and measure severity ranging from 0 to 100 considering various severity of arrhythmia (e.g. ventricular tachycardia is the most severe). These data are then randomized to a different location and fed to the visualization tool for functionally verify and assess the performance of the visualization tool. Furthermore, a survey was conducted to collect feedback about the visualization tool that shows that 81.4% participants in pre-session and 84.75% in post-session provided positive feedback about the visualization of health data. By using this framework, community members can generate collective knowledge that might assist community stakeholders such as the Health Department to improve community health by identifying health issues, developing strategies, and resource allocation

Advanced Radio Frequency Identification Design and Applications

Radio Frequency Identification (RFID) is a modern wireless data transmission and reception technique for applications including automatic identification, asset tracking and security surveillance. This book focuses on the advances in RFID tag antenna and ASIC design, novel chipless RFID tag design, security protocol enhancements along with some novel applications of RFID

Biomedical Engineering

Biomedical engineering is currently relatively wide scientific area which has been constantly bringing innovations with an objective to support and improve all areas of medicine such as therapy, diagnostics and rehabilitation. It holds a strong position also in natural and biological sciences. In the terms of application, biomedical engineering is present at almost all technical universities where some of them are targeted for the research and development in this area. The presented book brings chosen outputs and results of research and development tasks, often supported by important world or European framework programs or grant agencies. The knowledge and findings from the area of biomaterials, bioelectronics, bioinformatics, biomedical devices and tools or computer support in the processes of diagnostics and therapy are defined in a way that they bring both basic information to a reader and also specific outputs with a possible further use in research and development

Modeling EMI Resulting from a Signal Via Transition Through Power/Ground Layers

Signal transitioning through layers on vias are very common in multi-layer printed circuit board (PCB) design. For a signal via transitioning through the internal power and ground planes, the return current must switch from one reference plane to another reference plane. The discontinuity of the return current at the via excites the power and ground planes, and results in noise on the power bus that can lead to signal integrity, as well as EMI problems. Numerical methods, such as the finite-difference time-domain (FDTD), Moment of Methods (MoM), and partial element equivalent circuit (PEEC) method, were employed herein to study this problem. The modeled results are supported by measurements. In addition, a common EMI mitigation approach of adding a decoupling capacitor was investigated with the FDTD method

A Flexible, Low-Power, Programmable Unsupervised Neural Network Based on Microcontrollers for Medical Applications

We present an implementation and laboratory tests of a winner takes all (WTA) artificial neural network (NN) on two microcontrollers (μC) with the ARM Cortex M3 and the AVR cores. The prospective application of this device is in wireless body sensor network (WBSN) in an on-line analysis of electrocardiograph (ECG) and electromyograph (EMG) biomedical signals. The proposed device will be used as a base station in the WBSN, acquiring and analysing the signals from the sensors placed on the human body. The proposed system is equiped with an analog-todigital converter (ADC), and allows for multi-channel acquisition of analog signals, preprocessing (filtering) and further analysis

ESSE 2017. Proceedings of the International Conference on Environmental Science and Sustainable Energy

Environmental science is an interdisciplinary academic field that integrates physical-, biological-, and information sciences to study and solve environmental problems. ESSE - The International Conference on Environmental Science and Sustainable Energy provides a platform for experts, professionals, and researchers to share updated information and stimulate the communication with each other. In 2017 it was held in Suzhou, China June 23-25, 2017