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.

Bacterial infections have great public health and economic impact. While at present most can be treated with antibiotics, doing so requires cases of bacterial infections to be recognised early so that they can be treated with the right drugs, while ensuring that antibiotics are not given unnecessarily. With the growth in antibiotic resistance, it is becoming essential that we use these drugs appropriately. At present growth of organisms from patient samples (e.g. urine), a process which takes 18 hours or more, is usually required before specific infecting bacteria can identified. A device able to rapidly detect the presence of bacteria in such samples, and identify which species are present, without this growth step would enable doctors to make rapid and informed decisions about when antibiotic treatment is necessary and which drug should be used. Here we propose to develop and evaluate a technology for identifying bacteria in patient samples. We will combine a novel series of chemical probes (fluorescent carbon dots, FCDs) that can attach to bacteria to make them fluorescent, with an ultra-sensitive quantum photonic sensor (QPS) developed by our industrial partner, FluoretiQ Ltd., that is able to detect these fluorescent bacteria in patient samples. In order to identify individual species of bacteria we will attach specific sugars (glycans) to the surface of FCDs, exploiting the fact that different bacteria recognise particular sugar molecules as part of the process of binding to the cells of their host. We base our trials around E coli bacteria causing urinary tract infections as these are common conditions that create high workloads for NHS laboratories (our clinical partner processes up to 1000 urine samples per day) and if improperly treated can lead to severe conditions such as sepsis. We will test this methodology by assessing in the laboratory whether specific bacteria can bind to specific glycan-FCDs. A second series of laboratory experiments will then seek to replicate patient samples by suspending bacteria derived from patients, and cultured human cells, in liquid media designed to mimic the composition of human urine and testing whether glycan-FCDs bind bacteria under these conditions. Finally, with support from clinical microbiologists, we will test whether the glycan-FCD/QPS method can detect and identify bacteria in urine samples from human patients and evaluate its effectiveness compared to methods currently in use. As future users they will also help us to optimise the method and associated instrumentation to ensure that this can be used easily in the clinical laboratory, and provide guidance on how to ensure that our method can be validated against appropriate comparators and demonstrated to comply with NHS quality management systems. In parallel we will test whether glycan-FCDs can be used as the basis for new treatments for bacterial infections. We have already demonstrated that FCDs can bind to and enter bacteria; preliminary experiments show that they can also kill bacteria, in a light-dependent process. Hence we will investigate whether our modified glycan-FCDs retain the ability to kill bacteria, and whether this killing is specific to the species targeted by the particular surface sugar. We will also attach antibiotics to the surface of FCDs to test whether this represents a method to deliver drugs to specific bacteria, many of which are difficult to kill with antibiotics because the drug is unable to enter the bacterial cell. The project will establish whether glycan-FCDs can form the basis of a rapid method for detecting infecting bacteria in patient samples in the clinical microbiology laboratory, and whether these can also be used to improve the effectiveness of antibiotics against many of these organisms. In so doing we will also develop new methods for synthesising complex sugar molecules that may be applied in multiple other research areas including drug and vaccine development.

Breast cancer is the commonest cause of death in women between the ages of 35 and 55 in Europe. Worldwide, a woman will die from the disease every 13 minutes. Breast cancer is very much a survivable disease however it is vital that the tumour is caught at an early stage. This requires a national screening programme for all women (in addition to regular self-examination by women of their breasts). Unfortunately the existing screening techniques are not very ideal. X-ray for example, is only suitable for older women and is also quite uncomfortable. Even in these older (post-menopausal) women, it has quite high false-positive rates (resulting in women having unnecessary biopsies) and false-negative rates (in other words, it misses some tumours). There is no suitable routine screening technique available for younger women. The aim of this proposal is to continue research into a new imaging method based on UWB radar. This sends out a short burst of radio-waves into the breast and "listens" for reflections - these radio-waves are completely harmless and the imaging procedure is quick and comfortable. At the moment this new imaging technique is in its infancy and much work remains to be done if we are to reach the ultimate goal of a cheap, quick and comfortable breast imaging method for all women. Because the imaging method is harmless, it could be repeated as often as necessary and because it will be very cheap, it could be based in a GP surgery or even a van, rather than requiring a visit to hospital.

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.

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.