Although people do not usually talk at the same time, simultaneous speech by two or more speakers is surprisingly frequent. In the typical conversations recorded for our recent AHRC -funded project it occupies 16% of total talking time, 41% of speaker turns being overlapped by another speaker. Simultaneous or overlapping talk is known to be a particular problem for individuals who have a hearing loss, even when using a conventional hearing aid or cochlear implant. Until recently, even in one-to-one settings many users would need optimum conditions in order to hold a satisfactory conversation, e.g. a quiet environment and the communication awareness of both participants that they should avoid talking at the same time. Professionals have steered clear of advising cochlear implant users about how to deal with situations of overlapping talk, on the basis that such a situation would be just too hard to handle. However, recent improvements in the signal processing strategies used in cochlear implants mean that it is now more realistic for users to attempt to engage in conversations where overlapping talk occurs. The aim of this follow-on project is to engage with a group of adult users of cochlear implants in order to develop useful training materials for handling overlapping talk in conversation. These materials will draw mainly on the outputs from our earlier project on overlapping talk, where we have developed a unique corpus and some key findings about overlapping talk in normal conversation. To the best of our knowledge, these will be the first materials that specifically address the problems raised by overlapping talk. The main objectives of this project are: 1. To identify the specific issues that overlapping talk raises for cochlear implant users This will be accomplished by direct questioning, via focus group and questionnaire survey; observation of recorded naturalistic conversations; and by exploring linguistic and cultural differences. 2. To develop ways of improving the experience of cochlear implant users This will involve devising training software and activities for cochlear implant users, in close collaboration with a group of cochlear implant users. The idea is that the implant user will be able to work with the materials on their own, and with their family members, at home. The software will make use of real examples from our collection of recorded conversations, where people are quite often talking in overlap. The software will focus on both listening and speaking. On the listening side, for example, it will allow users to simultaneously hear and visualise the flow of a conversation over time, by presenting speaker activity on a graphical timeline. On the speaking side, it will provide learning tasks that allow users to practise producing cues in their own speech. 3. To promote and disseminate the training software through a special event, a dedicated website and through existing channels for cochlear implant users and professionals. This project will engage with a small number of cochlear implant users initially, in order to develop software materials that can assist cochlear implant users when dealing with overlapping talk. The project is embedded in the local NHS cochlear implant service, in which two project team members are employed. Initially the outputs of the project will benefit users of that service. They will be disseminated more widely in the UK through the final dissemination event, and through participation in established national meetings for professionals and for cochlear implant users. Other team members will disseminate results at national and international conferences, which will raise awareness of the work within relevant academic communities. The project website will provide global access to the software materials: the project therefore has the potential to enhance the social participation of more than a quarter of a million cochlear implant users worldwide.

Medical diagnostic tests performed in high throughput, time critical NHS hospital laboratories are key to ensuring that clinicians can deliver high quality patient care. An important type of test, providing diagnosis for a wide range of diseases and illnesses, including cancer and heart problems, are immunoassays. These assays are based on nature's exquisite recognition apparatus: anti-bodies. Immunoassays involve attaching anti-bodies to a detector surface, waiting for the analyte (e.g. protein biomarkers or specific cells) of interest to become bound at the surface, and finally using electrical or optical methods to read-out the test result. However, due to the low concentration of many diagnostic analytes, the time spent waiting for sufficient amounts of analyte to diffuse to the detector to enable read-out can be significant. The consequence of these long incubation times is severe: for example automated hospital instruments that can handle thousands of samples per hour are rate limited by up to twenty minute waits for the immunoassay process to complete. As well as reducing throughput for routine analysis, these delays hamper the task of returning time critical diagnostic information to clinicians, such as screening for heart problems in patients with chest pain. Slow accumulation of analyte at an anti-body detector also limits developing methods that rely on isolating rare cells, such as circulating tumour cells to indicate the progression of cancer and enable personalised medicine. In this context, it is clear that the challenge of speeding up the rate at which analytes reach the detector is great, and that successfully achieving this can have significant Healthcare impact. Here we propose to develop a new approach to achieve rapid analyte detection, by exploiting micro-rockets; small scale devices that can generate rapid motion within fluids. Micro-rockets are powered by the asymmetrical release of bubbles from their surface. These bubbles are generated by enzymes decomposing fuel molecules in the surrounding solution. Micro-rockets will be used to speed up immunoassays in two ways. Firstly, micro-rockets' rapid motion and bubble generation stirs solutions, which is otherwise hard to achieve at small scales. This will be used to reduce the incubation times for immunoassays where anti-bodies are attached to the inside surfaces of a "micro-well" containing the analytical solution. By agitating the solution with micro-rockets, analytes will contact the well surfaces more frequently, speeding up detection. In the second method, the micro-rockets themselves will be covered with anti-bodies and used as a mobile detector, rapidly moving throughout the analytical sample. The fast motion will allow dilute quantities of analyte to be rapidly located. Analyte binding rate to anti-bodies and selectivity will also be improved by using a rapidly moving detector surface. At the end of the incubation period, magnets will be used to retrieve the dispersed rockets to enable analyte concentration to be determined using existing optical or electrical methods. Efficiently developing new micro-rockets with the required functions of analyte recognition and magnetic control will be aided by using ink-jet printing to allow micro-rocket composition, size, shape to be easily controlled and optimised. To demonstrate the utility of micro-rockets, experiments will be conducted to compare the speed at which micro-rockets can acquire analytes, compared to the existing diagnostic methods used by hospitals. Two diagnostic tests will be considered: one for protein molecules called "Troponins" that signal recent cardiac damage, and the second for circulating tumour cells. Establishing proof of micro-rocket effectiveness in this way will be a key step to attract interest from industrial partners who can assist the development of this technology to allow eventual deployment in hospitals.

The most common cause of admission to the intensive care unit is septicaemia or sepsis1, which produces septic shock2, which is also a process that often results in death that follows multi-organ failure. The mechanism of sepsis affects not just the area of the body where infection or a triggering 'insult' occurs, but triggers a cascade of inflammation and inappropriate blood clotting in the small vessels, that can spread throughout the body damaging many body. The two organs systems that typically need most support during this time are the respiratory and cardiovascular systems. In order to address this pressing need to unravel the underlying phenomena associated with ventilator/patient interactions and septic shock treatment there is need for an integrated research strategy. Hence, the aim of this project is to 'dynamically' chart (predict) the clinical state of patients during the acute phase of sepsis by integrating for the first time various types of 'knowledge nodes' from respiratory and cardiovascular functions. Such nodes will combine mechanistic models driven by physiology, data-driven models elicited via experimental data, linguistic knowledge emanating from clinical experts, and discrete discontinuous data. The information included in this dynamic chart (map) will be specific to the treatment therapies subscribed to the patients but will not be patient-specific since the hybrid nature of the information included will lend itself automatically to generalising properties following intra and inter patient parameter variability. Ultimately, this information will be used to design an integrated intelligent decision support system that is able to merge (fuse) the various types of knowledge and multi-source data for appropriate and effective therapy. The system will be based on a through patient modelling approach from the patient's history prior to being admitted to hospital to beat-to-beat clinical data subsequently, until his/her final discharge from hospital. As new patient data is gathered the patient hybrid model will be updated dynamically using an 'incremental learning' strategy which consists of only supplementing the current model information with the 'new' knowledge without disrupting the original optimised old model. In addition, the decision support system is improved through on-line learning with the reward/punishment scheme for good/bad therapy decisions respectively while drawing further experiences with other patients with similar conditions.

Traditionally, engineering design relies on scale separation. Virtually every physical process involves complex interactions across several space-time scales. For most engineering problems it is assumed that processes at different scales can be represented at the larger scale through some averaged property. However, when such assumptions of scale separation cannot be made, as in the modelling of biological systems where scales overlap, the inherent complexity of multi-scale interaction cannot be avoided. This proposal focuses on the establishment of a currently non-existent but essential computational platform for the treatment of musculoskeletal disorders. A multi-scale process in human physiology can be modelled as a collection of single scale models acting on a specific level of biological organisation coupled together across scales using appropriate scale bridging methods. Several unresolved challenges in the biomedical field have inhibited the development of predictive models needed in personalised medicine including (i) How to link mixed multiphysics models across several space-time scales. (ii) How to replace unobservable (possibly invasive and hence expensive) variables and states using proxy measurements (non invasive) reconstructed from the observable variables. (iii) How to use population data across patient classes or animal proxies to accommodate missing data. (iv) How to model uncertainty and the propagation of this across the simulations. (v) How to achieve these objectives within a framework that can be mapped to other engineering problems. This proposal will tackle each of these challenges with: - the development of a multi-scale model of the musculoskeletal system that describes the mechanobiological processes from the whole body (neuromuscular control and body dynamics) down to the cellular level (bone remodelling and mechanosensing); - the creation of a multi-scale model from a partially identified input obtained by fusing a generic atlas of the anatomy, physiology, biology, and biomechanics for each individual. This framework will be integrated in an efficient hypermodelling approach, numerically optimised at each scale level. Once fully realised, such a multi-scale framework will enable (i) deployment of specialised implementations as decision-support systems for diagnosis, prognosis, and treatment planning and monitoring for specific skeletal diseases such as lower back pain, osteoporosis, bone tumours and secondary metastases and osteoarthritis; (ii) implementation of in silico clinical trials for new orthopaedic and tissue engineering implants, modelling the variability of populations, providing a more accurate pre-clinical assessment for musculoskeletal devices and predicting the clinical outcome of these new devices; (iii) optimised interventions with respect to high socioeconomic impact conditions such as obesity, ageing population, disabilities, and chronic diseases in relation to physical activity, and assistive and rehabilitative technologies for neuromuscular deficits.

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.