Microwave Imaging (MI) has gained a great deal of attention among researchers over the past decade, mainly due to its potential use in breast cancer imaging. MI is seen as a safe, portable and low-cost alternative to existing imaging modalities. Due to the breast tissue properties at microwave frequencies, MI benefits from significantly higher contrast than other techniques. The great excitement about MI radar system is that, using a multi-static real aperture technique and sophisticated signal processing, it has sufficient resolution to be clinically useful and is far better than simple wavelength assumptions would estimate. Whilst to date MI has been mainly proposed for breast cancer detection, some recent reports have also speculated a use of MI in extremities imaging, diagnostics of lung cancer, brain imaging and cardiac imaging. Despite the interest in Microwave Imaging among researchers, it has not moved far beyond numerical simulations and very simple experimental works without clinical realisation. Bristol is among two research groups in the world who have clinical experience with Microwave Imaging.Compared with other medical imaging techniques, microwave imaging is still in its infancy. One historical reason for this might due to the fact that most microwave systems-devices originated in military applications, radar being an obvious example. In recent years however, due to the mobile/wireless revolution, we have witnessed unprecedented progress in high performance microwave hardware as well as computing power. This opens up a unique opportunity for development of microwave imaging systems. The goal of this Career Acceleration Fellowship project is to explore a novel direction in MI, Differential Microwave Imaging (DMI), in clinical applications reaching far beyond breast cancer detection. In Differential Microwave Imaging, the goal is to image temporal changes in tissue, and not the tissue itself. This somewhat limits usability of DMI as an imaging technique on one hand, but at the same time it opens up totally new applications where standard Microwave Imaging could not be applied. The idea of DMI came from the discovery during world's first clinical trial of microwave radar imaging system in Bristol in 2009. During the clinical trials it was realised that the Microwave Imaging system was extremely sensitive to any changes occurring during the scan. Following this up it was then discovered that the local change in tissue properties can easily be detected and precisely located. Moreover, it was shown that this change in local properties of tissues can even be detected in very dense and heterogeneous breast tissues. The project will focus on two applications, serving as Proof of Principle:1. Nanoparticle contrast-enhanced DMI for cancer detection The proposed work on 3D detection of nanoparticles is of great interest to researchers working in the cancer imaging field. DMI could find applications not only in cancer detection, but it could also be used to find and evaluate the effectiveness of new cancer biomarkers, track nanoparticle-labelled cells or monitor delivery of nanoparticles for hyperthermia treatment. 2. Functional brain imaging using DMI radar systemDMI, as a general method, is also a promising concept for functional brain imaging. Development of the DMI system for functional brain imaging is timely related to current research activities in neuroscience. Functional imaging is used to diagnose metabolic diseases and lesions (such as Alzheimer's disease or epilepsy) and also for neurological and cognitive psychology research. This novel interdisciplinary project connects the fields of electronic engineering, nanotechnology and medical physics. The proposed research project addresses one of the EPSRC strategic priorities: Towards next generation healthcare. High calibre of clinical collaborators will ensure that research outcomes are relevant to end users.

This project will develop and validate exciting novel ways in which people can interact with the world via cognitive wearables -intelligent on-body computing systems that aim to understand the user, the context, and importantly, are prompt-less and useful. Specifically, we will focus on the automatic production and display of what we call glanceable guidance. Eschewing traditional and intricate 3D Augmented Reality approaches that have been difficult to show significant usefulness, glanceable guidance aims to synthesize the nuances of complex tasks in short snippets that are ideal for wearable computing systems and that interfere less with the user and that are easier to learn and use. There are two key research challenges, the first is to be able to mine information from long, raw and unscripted wearable video taken from real user-object interactions in order to generate the glanceable supports. Another key challenge is how to automatically detect user's moments of uncertainty during which support should be provided without the user's explicit prompt. The project aims to address the following fundamental problems: 1. Improve the detection of user's attention by robustly determining periods of time that correspond to task-relevant object interactions from a continuous stream of wearable visual and inertial sensors. 2. Provide assistance only when it is needed by building models of the user, context and task from autonomously identified micro-interactions by multiple users, focusing on models that can facilitate guidance. 3. Identify and predict action uncertainty from wearable sensing in particular gaze patterns and head motions. 4. Detect and weigh user expertise for the identification of task nuances towards the optimal creation of real-time tailored guidance. 5. Design and deliver glanceable guidance that acts in a seamless and prompt-less manner during task performance with minimal interruptions, based on autonomously built models. GLANCE is underpinned by a rich program of experimental work and rigorous validation across a variety of interaction tasks and user groups. Populations to be tested include skilled and general population and for tasks that include: assembly, using novel equipment (e.g. an unknown coffee maker), and repair tasks (e.g. replacing a bicycle gear cable). It also tightly incorporates the development of working demonstrations. And in collaboration with our partners the project will explore high-value impact cases related to health care towards assisted living and in industrial settings focusing on assembly and maintenance tasks. Our team is a collaboration between Computer Science, to develop a the novel data mining and computer vision algorithms, and Behavioral Science to understand when and how users need support.

Our Hub brings together a team of mathematicians, statisticians and clinicians with a range of industrial partners, patients and other stakeholders to focus on the development of new quantitative methods for applications to diagnosing and managing long-term health conditions such as diabetes and psychosis and combating antimicrobial infections such as sepsis and bronchiectasis. This approach is underpinned by the world-leading expertise in diabetes, microbial communities, medical mycology and mental health concentrated at the University of Exeter. It uses the breadth of theoretical and methodological expertise of the Hub's team to give innovative approaches to both research and translational aspects. Although quantitative modelling is a well-established tool used in the fields of economics and finance, cutting-edge quantitative analysis has only recently become possible in health care. However, up to now it has been restricted to health economics in the context of healthcare services and systems management. Applications to develop future therapies, optimising treatments and improving community health and care are in its infancy. This is due to a number of challenges from both mathematical (methodological) as well as clinical and patients' perspectives. Our Hub approach will allow us to develop novel statistical and mathematical methodologies of relevance to our clinical and industrial partners, informed by relevant patient groups. Building this new generation of quantitative models requires that we advance our mathematical understanding of the effective network interaction and emergent patterns of health and disease. Clinical translation of mathematical and statistical advances necessitates that we further develop robust uncertainty quantification methodology for novel therapy, treatment or intervention prediction and evaluation. NHS long-term planning aspires to deliver healthcare that is more personalised and patient centred, more focused on prevention, and more likely to be delivered in the community, out of hospital. Our Hub will contribute to this through developing mathematical and statistical tools needed to inform clinical decision making on a patient-by-patient basis. The basis of this approach is quantitative patient-specific mathematical models, the parameters of which are determined directly from individual patient's data. As an example of this, our recent research in the field of mental health has revealed that movement signatures could be used to distinguish between healthy subjects and patients with schizophrenia. This hypothesis was tested in a cohort of people with schizophrenia and we developed a quantitative analysis pipe line allowing for classification of individuals as healthy or patients. The features used for classification involving data-driven models of individual movement properties as well as measures of coordination with a virtual partner were proposed as a novel biomarker of social phobias. To validate this in an NHS setting, we have recently carried out a feasibility study in collaboration with the early intervention for psychosis teams in Devon Partnership Mental Health Trust. The success of this study could significantly advance the early detection of psychosis by enabling diagnosis using novel markers that are easily measured and analysed and improve accuracy of diagnosis. Indeed, personalised quantitative models hold the promise for transforming prognosis, diagnosis and treatment of a wide range of clinical conditions. For example, in diabetes where a range of treatment options exist, identifying the optimal medication, and the pattern of its delivery, based upon the profile of the individual will enable us to maximise efficacy, whilst minimising unwanted side effects.

This project, in partnership with Biocontrol Ltd and the departments of Chemistry and Chemical Engineering at the University of Bath, will encapsulate specific lytic phages within phospholipid vesicles, and incorporate the vesicles into a prototype burn / wound dressing and a topical aqueous cream. The primary focus of the work is in the prevention of infection of paediatric burns, where our clinical partner, Dr Amber Young at the South West Paediatric Burns Centre, Frenchay hospital will provide expertise. The vesicles will be designed such that they both will stabilize the phage over time i.e. when stored, but only release their contents following exposure to secreted toxins and enzymes from pathogenic bacteria. The aim of this project is to reduce the risk of infection from burns and other injuries by making a 'smart' dressing, based on phage therapeutics.38,000 children on average suffer burn injuries in England and Wales each year, of which 55% are scalds. Most are small in area, 80% are in children under five years and the majority are due to hot drink spillages. One of the primary problems in the treatment of burns is bacterial infection, which can delay healing, increase pain; increase the risk of scarring and in some cases cause death. In recent years there have been great improvements in the treatment of burns, particularly with biologically-derived dressings which actively promote cell growth. However, the problem of infection has not gone away, and there is evidence that silver treated antimicrobial dressings can delay burn healing.

The EMERGENCE network aims to create a sustainable eco-system of researchers, businesses, end-users, health and social care commissioners and practitioners, policy makers and regulatory bodies in order to build knowledge and capability needed to enable healthcare robots to support people living with frailty in the community. By adopting a person-centred approach to developing healthcare robotics technology we seek to improve the quality of life and independence of older people at risk of, and living with frailty, whilst helping to contain spiralling care costs. Individuals with frailty have different needs but, commonly, assistance is needed in activities related to mobility, self-care and domestic life, social activities and relationships. Healthcare can be enhanced by supporting people to better self-manage the conditions resulting from frailty, and improving information and data flow between individuals and healthcare practitioners, enabling more timely interventions. Providing cost-effective and high-quality support for an aging population is a high priority issue for the government. The lack of adequate social care provisions in the community and funding cuts have added to the pressures on an already overstretched healthcare system. The gaps in ability to deliver the requisite quality of care, in the face of a shrinking care workforce, have been particularly exposed during the ongoing Covid-19 crisis. Healthcare robots are increasingly recognised as solutions in helping people improve independent living, by having the ability to offer physical assistance as well as supporting complex self-management and healthcare tasks when integrated with patient data. The EMERGENCE network will foster and facilitate innovative research and development of healthcare robotic solutions so that they can be realised as pragmatic and sustainable solutions providing personalised, affordable and inclusive health and social care in the community. We will work with our clinical partners and user groups to translate the current health and social care challenges in assessing, reducing and managing frailty into a set of clear and actionable requirements that will inspire novel research and enable engineers to develop appropriate healthcare robotics solutions. We will also establish best practice guidelines for informing the design and development of healthcare robotics solutions, addressing assessment, reduction and self-management of frailty and end-user interactions for people with age-related sensory, physical and cognitive impairments. This will help the UK develop cross-cutting research capabilities in ethical design, evaluation and production of healthcare robots. To enable the design and evaluation of healthcare robotic solutions we will utilize the consortium's living lab test beds. These include the Assisted Living Studio in the Bristol Robotics Lab covering the South West, the National Robotarium in Edinburgh together with the Health Innovation South East Scotland's Midlothian test bed, the Advanced Wellbeing Research Centre and HomeLab in Sheffield, and the Robot House at the University of Hertfordshire covering the South East. Up to 10 funded feasibility studies will drive co-designed, high quality research that will lead to technologies capable of transforming community health and care. The network will also establish safety and regulatory requirements to ensure that healthcare robotic solutions can be easily deployed and integrated as part of community-based frailty care packages. In addition, we will identify gaps in the skills set of carers and therapists that might prevent them from using robotic solutions effectively and inform the development of training content to address these gaps. This will foster the regulatory, political and commercial environments and the workforce skills needed to make the UK a global leader in the use of robotics to support the government's ageing society grand challenge.