Textile UHF-RFID antenna sensors based on material features, interfaces and application scenarios

Tesi en modalitat de compendi de publicacions, amb una secció retallada per drets de l'editor. In reference to IEEE copyrighted material which is used with permission in this thesis, the IEEE does not endorse any of Universitat Politècnica de Catalunya's products or services. Internal or personal use of this material is permitted. If interested in reprinting/republishing IEEE copyrighted material for advertising or promotional purposes or for creating new collective works for resale or redistribution, please go to http://www.ieee.org/publications_standards/publications/rights/rights_link.html to learn how to obtain a License from RightsLink.Radio frequency identification over measurable ultra-high frequency textile substrates (UHF-RFID) is a promising technology to develop new applications in the field of health and the Internet of Things (IOT), due to the massive use of fabrics and the technological maturity of embroidery techniques. This thesis is the result of a compendium of publications on this topic. First, as a result of the analysis of the state of art, a systematic review entitled 'Wearable textile UHF-RFID sensors: A systematic review' has been published. The thesis aims to improve research on UHF-RFID textile-based sensor technology. Thanks to the analysis of the state of art, three novel research objectives have been set that are worth exploring. The first is to study novel detection functions for textile UHF-RFID based sensor technology; the second is to find a connection/interface solution between textile antennas and integrated circuit (IC) chips and the third is to reduce the costs of such technology to promote future commercial applications. To contextualize the thesis, it includes the necessary theoretical fundamentals and the manufacturing and characterization methods used during it. As a result of the work derived from the first objective, a scientific article entitled “Textile UHF-RFID Antenna Sensor for Measurements of Sucrose Solutions in Different Levels of Concentration” has been published. In this work, a textile UHF-RFID tag with two detection positions is proposed for sucrose solution measurements. The two detection positions with the different detection functions show good performance and can offer two options for future full applications. In addition, another scientific article entitled “ Textile UHF-RFID Antenna Embroidered on Surgical Masks for Future Textile Sensing Applications” has been published to support the first objective. The inspiration for this work came from the current pandemic situation. This work develops three progressive designs of textile UHF-RFID antennas over surgical masks due to the current global epidemic situation. Reliability testing demonstrated that the proposed designs can be used for human healthcare focused applications. As a result of the second objective, a research article entitled 'Experimental Comparison of Three Electro-textile Interfaces for Textile UHF-RFID Tags on Clothes' has been published. This work proposes three electro-textile interfaces integrated with the corresponding textile UHF-RFID antennas and provides the chip-textile connection solutions (through sewing, push buttons and insertion). As a result of this objective, an electro-textile interconnect system has been proposed together with its electrical model, which allows the correct adaptation of impedances between the RFID antennas and the integrated circuit. It is worth noting that the mixed-use feasibility of the proposed electro-textile interfaces and the designed textile UHF-RFID antennas has been verified, reducing the cost in the design procedure in applications where the read range requirements of the order of 1 meter. The third objective has been achieved and exposed by a scientific article entitled 'Electro-textile UHF-RFID Compression Sensor for Health-caring Applications'. It proposes a single UHF-RFID based compression textile sensor that can be used simultaneously in two different healthcare application scenarios, which directly impacts on cost reduction.La identificación por radiofrecuencia sobre substratos textiles de ultra alta frecuencia (UHF-RFID) con capacidad de medida es una tecnología prometedora para desarrollar nuevas aplicaciones en el campo de la salud y el Internet de las cosas (IOT), debido a la masiva utilización de los tejidos y a la madurez tecnológica de las técnicas de bordado. Esta tesis es el resultado de un compendio de publicaciones sobre dicha temática. En primer lugar, como resultado del análisis del estado del arte se ha publicado una revisión sistemática titulada 'Wearable textile UHF-RFID sensors: A systematic review'. La tesis tiene como objetivo mejorar la investigación sobre la tecnología de sensores basada en textiles UHF-RFID. Gracias al análisis del estado del arte se han fijado tres objetivos de investigación novedosos que vale la pena explorar. El primero es estudiar funciones de detección novedosas para la tecnología de sensores basada en UHF-RFID textiles; el segundo es encontrar una solución de conexión/interfaz entre antenas textiles y chips de circuito integrado (IC) y el tercero es la reducción de costes de dicha tecnología para promover futuras aplicaciones comerciales. Para contextualizar la tesis, ésta incluye los fundamentos teóricos necesarios y los métodos de fabricación y caracterización utilizados durante la misma. Como resultado del trabajo derivado del primer objetivo, se ha publicado un artículo científico titulado “Textile UHF-RFID Antenna Sensor for Measurements of Sucrose Solutions in Different Levels of Concentration”. En este trabajo, se propone una etiqueta UHF-RFID textil con dos posiciones de detección para mediciones de solución de sacarosa. Las dos posiciones de detección con las diferentes funciones de detección muestran un buen rendimiento y pueden ofrecer dos opciones para futuras aplicaciones completas. Además, se ha publicado otro artículo científico titulado "Textile UHF-RFID Antenna Embroidered on Surgical Masks for Future Textile Sensing Applications" para respaldar el primer objetivo. La inspiración para este trabajo vino de la actual situación de pandemia. En este trabajo se desarrollan tres diseños progresivos de antenas UHF-RFID textiles sobre mascarillas quirúrgicas debido a la situación epidémica mundial actual. Las pruebas de fiabilidad demostraron que los diseños propuestos se pueden usar para aplicaciones centradas en el cuidado de las personas. Como resultado del segundo objetivo, se ha publicado un artículo de investigación titulado 'Experimental Comparison of Three Electro-textile Interfaces for Textile UHF-RFID Tags on Clothes'. En este trabajo se proponen tres interfaces electro-textiles integradas con las correspondientes antenas UHF-RFID textiles y se aportan las soluciones de conexión chip-textil (mediante costura, botones a presión e inserción). Como resultado de este objetivo, se ha propuesto un sistema de interconexión electro-textil junto con su modelo eléctrico, lo que permite la correcta adaptación de impedancias entre las antenas RFID y el circuito integrado. Vale la pena señalar que se ha verificado la viabilidad de uso mixto de las interfaces electro-textiles propuestas y las antenas UHF-RFID textiles diseñadas, lo que reduce el coste en el procedimiento de diseño en aplicaciones donde los requerimientos de rango de lectura del orden de 1 metro. El tercer objetivo se ha alcanzado y expuesto mediante un artículo científico titulado 'Electro-textile UHF-RFID Compression Sensor for Health-caring Applications'. En él, se propone un único sensor textil de compresión basado en UHF-RFID que puede ser utilizado a la vez en dosPostprint (published version

Super low resolution RF powered accelerometers for alerting on hospitalized patient bed exits

Falls have serious consequences and are prevalent in acute hospitals and nursing homes caring for older people. Most falls occur in bedrooms and near the bed. Technological interventions to mitigate the risk of falling aim to automatically monitor bed-exit events and subsequently alert healthcare personnel to provide timely supervisions. We observe that frequency-domain information related to patient activities exist predominantly in very low frequencies. Therefore, we recognise the potential to employ a low resolution acceleration sensing modality in contrast to powering and sensing with a conventional MEMS (Micro Electro Mechanical System) accelerometer. Consequently, we investigate a batteryless sensing modality with low cost wirelessly powered Radio Frequency Identification (RFID) technology with the potential for convenient integration into clothing, such as hospital gowns. We design and build a passive accelerometer-based RFID sensor embodiment-ID-Sensor-for our study. The sensor design allows deriving ultra low resolution acceleration data from the rate of change of unique RFID tag identifiers in accordance with the movement of a patient's upper body. We investigate two convolutional neural network architectures for learning from raw RFID-only data streams and compare performance with a traditional shallow classifier with engineered features. We evaluate performance with 23 hospitalized older patients. We demonstrate, for the first time and to the best of knowledge, that: i) the low resolution acceleration data embedded in the RF powered ID-Sensor data stream can provide a practicable method for activity recognition; and ii) highly discriminative features can be efficiently learned from the raw RFID-only data stream using a fully convolutional network architecture.Michael Chesser, Asangi Jayatilaka, Renuka Visvanathany, Christophe Fumeauxz Alanson Samplex and Damith C. Ranasingh

Deep Learning Methods for Human Activity Recognition using Wearables

Wearable sensors provide an infrastructure-less multi-modal sensing method. Current trends point to a pervasive integration of wearables into our lives with these devices providing the basis for wellness and healthcare applications across rehabilitation, caring for a growing older population, and improving human performance. Fundamental to these applications is our ability to automatically and accurately recognise human activities from often tiny sensors embedded in wearables. In this dissertation, we consider the problem of human activity recognition (HAR) using multi-channel time-series data captured by wearable sensors. Our collective know-how regarding the solution of HAR problems with wearables has progressed immensely through the use of deep learning paradigms. Nevertheless, this field still faces unique methodological challenges. As such, this dissertation focuses on developing end-to-end deep learning frameworks to promote HAR application opportunities using wearable sensor technologies and to mitigate specific associated challenges. In our efforts, the investigated problems cover a diverse range of HAR challenges and spans from fully supervised to unsupervised problem domains. In order to enhance automatic feature extraction from multi-channel time-series data for HAR, the problem of learning enriched and highly discriminative activity feature representations with deep neural networks is considered. Accordingly, novel end-to-end network elements are designed which: (a) exploit the latent relationships between multi-channel sensor modalities and specific activities, (b) employ effective regularisation through data-agnostic augmentation for multi-modal sensor data streams, and (c) incorporate optimization objectives to encourage minimal intra-class representation differences, while maximising inter-class differences to achieve more discriminative features. In order to promote new opportunities in HAR with emerging battery-less sensing platforms, the problem of learning from irregularly sampled and temporally sparse readings captured by passive sensing modalities is considered. For the first time, an efficient set-based deep learning framework is developed to address the problem. This framework is able to learn directly from the generated data, bypassing the need for the conventional interpolation pre-processing stage. In order to address the multi-class window problem and create potential solutions for the challenging task of concurrent human activity recognition, the problem of enabling simultaneous prediction of multiple activities for sensory segments is considered. As such, the flexibility provided by the emerging set learning concepts is further leveraged to introduce a novel formulation of HAR. This formulation treats HAR as a set prediction problem and elegantly caters for segments carrying sensor data from multiple activities. To address this set prediction problem, a unified deep HAR architecture is designed that: (a) incorporates a set objective to learn mappings from raw input sensory segments to target activity sets, and (b) precedes the supervised learning phase with unsupervised parameter pre-training to exploit unlabelled data for better generalisation performance. In order to leverage the easily accessible unlabelled activity data-streams to serve downstream classification tasks, the problem of unsupervised representation learning from multi-channel time-series data is considered. For the first time, a novel recurrent generative adversarial (GAN) framework is developed that explores the GAN’s latent feature space to extract highly discriminating activity features in an unsupervised fashion. The superiority of the learned representations is substantiated by their ability to outperform the de facto unsupervised approaches based on autoencoder frameworks. At the same time, they rival the recognition performance of fully supervised trained models on downstream classification benchmarks. In recognition of the scarcity of large-scale annotated sensor datasets and the tediousness of collecting additional labelled data in this domain, the hitherto unexplored problem of end-to-end clustering of human activities from unlabelled wearable data is considered. To address this problem, a first study is presented for the purpose of developing a stand-alone deep learning paradigm to discover semantically meaningful clusters of human actions. In particular, the paradigm is intended to: (a) leverage the inherently sequential nature of sensory data, (b) exploit self-supervision from reconstruction and future prediction tasks, and (c) incorporate clustering-oriented objectives to promote the formation of highly discriminative activity clusters. The systematic investigations in this study create new opportunities for HAR to learn human activities using unlabelled data that can be conveniently and cheaply collected from wearables.Thesis (Ph.D.) -- University of Adelaide, School of Computer Science, 202

Co-creation of a person-centred integrated digital health model of care for fragility hip fractures: a mixed methods pragmatic research

Hip fracture is among the most devastating events faced by older people. These fragility fractures often occur due to trivial or low trauma falls. Current treatment efforts have led to best practice management guidelines and clinical audits at the level of the acute hospital setting and, to a certain extent, immediate post-hospital discharge. However, concerns still exist in the areas of prevention and rehabilitation outcomes including quality of life, and functional independence. Based on emerging evidence, a more nuanced approach is required for future health services delivery which incorporates: 1) musculoskeletal health; 2) increasing burden of multimorbidities; and 3) societal influences and circumstances shaping individual’s health literacy including access to digital technology. The aim of this thesis work is to conduct a program of research focused on establishing a personcentred and integrated model of care for older people with hip fractures assisted by digital health technology and modern educational approaches. The goal is to improve outcomes such as health literacy, access, functional rehabilitation, and quality of life. Objectives 1. To map out digital health interventions by conducting a comprehensive systematic review, which evaluates the effectiveness of digital health supported targeted patient communication versus usual provision of health information, on the recovery from fragility fractures. 2. To determine different phases of a research program for the development of a digital health hub enabled model of care focused on hip fracture rehabilitation through a dynamic conceptual framework. 3. To understand the perspective of older people with hip fractures, their family members, and residential aged carers, to inform the development of a personalised digital health hub and factors impacting the likelihood of potential usage. 4. To understand the perspectives of clinicians from various medical and surgical disciplines, allied health, and other relevant non-health stakeholders to inform the development of a digital health enabled model of care for fragility fractures. 5. To examine the process and management of innovation, and the strategic directions required to improve musculoskeletal healthcare at macro (policy), meso (service delivery), and micro (clinical practice) levels and discuss the critical role of different stakeholders in driving innovations in healthcare. 6. To describe a vision for future health care to address increasing population multimorbidity through the co-creation of personalised digital health hubs that recognise the importance of patient agency in driving the evolution of health services. This study emphasises that digital health solutions must be co-created and co-implemented by engaging relevant stakeholders including end consumers at the local contextual level. Developed countries such as Australia are emerging global leaders in contemporary research focused on advancing knowledge and filling gaps within existing health service delivery for older people.Thesis (Ph.D.) -- University of Adelaide, Adelaide Medical School, 202

Intelligent Circuits and Systems

ICICS-2020 is the third conference initiated by the School of Electronics and Electrical Engineering at Lovely Professional University that explored recent innovations of researchers working for the development of smart and green technologies in the fields of Energy, Electronics, Communications, Computers, and Control. ICICS provides innovators to identify new opportunities for the social and economic benefits of society.　 This conference bridges the gap between academics and R&D institutions, social visionaries, and experts from all strata of society to present their ongoing research activities and foster research relations between them. It provides opportunities for the exchange of new ideas, applications, and experiences in the field of smart technologies and finding global partners for future collaboration. The ICICS-2020 was conducted in two broad categories, Intelligent Circuits & Intelligent Systems and Emerging Technologies in Electrical Engineering

Life Sciences Program Tasks and Bibliography for FY 1997

This document includes information on all peer reviewed projects funded by the Office of Life and Microgravity Sciences and Applications, Life Sciences Division during fiscal year 1997. This document will be published annually and made available to scientists in the space life sciences field both as a hard copy and as an interactive internet web page

Life Sciences Program Tasks and Bibliography for FY 1996

This document includes information on all peer reviewed projects funded by the Office of Life and Microgravity Sciences and Applications, Life Sciences Division during fiscal year 1996. This document will be published annually and made available to scientists in the space life sciences field both as a hard copy and as an interactive Internet web page

Life Sciences Program Tasks and Bibliography

This document includes information on all peer reviewed projects funded by the Office of Life and Microgravity Sciences and Applications, Life Sciences Division during fiscal year 1995. Additionally, this inaugural edition of the Task Book includes information for FY 1994 programs. This document will be published annually and made available to scientists in the space life sciences field both as a hard copy and as an interactive Internet web pag