This multidisciplinary project will exploit an established UK based team's track record comprising RF & bio-sensing engineers, battery & materials scientists, and CPI, the UK National Catapult for Printed Electronics. Centred around Additive Manufacture and aimed towards scale-up, we will transform nascent wireless skin-based sensing to the high data rate capacity offered by upcoming communications systems using license-free 24 GHz channels. This will enable new streaming of biodata for remote diagnostics, monitoring and care, as well as ultra-low impact wireless EEG for forehead/ear/hair free regions. It will make possible the use of multiple sensing tags on multiple people simultaneously monitoring physiological parameters such as accelerometery (for activity tracking), photoplesmography (for heart rate monitoring), and sweat (for metabolite monitoring). At high data rate, this represents a step change over available technologies. Manufactured on highly flexible, potentially stretchable, substrates the skin tags take the form factor of temporary tattoos and are highly long lasting, discrete for social acceptability, and can follow the micro-contours of the skin to give a large contact surface area and consequently sensing signal-to-noise ratio. To achieve our aims, we will advance wireless mmWave devices, on-skin electronics, low-power bio-sensing, and additive manufacture. Additionally, through CPI, we will develop scale-up processes for these mmWave devices. Through existing investments the applicant team is positioning the UK for the large scale manufacture of on-skin sensor tags. EP/P027075/1 is creating an inkjet printing based manufacturing process for sensors on flexible substrates which avoids cleanrooms, uses graphene based ink formulations for biodegradability, and can be scaled up large run roll-to-roll screen printing. EP/R02331X/1 added the capability to print TiO2/LiFePO4 batteries integrated into the platform, removing a key integration bottleneck. This new proposal 'MultiSense' seeks to build upon the manufacturing base created by these two projects, extending it to overcome the key sensing limitation of current on skin tags: that they can only monitor one parameter from one person at a time, and at a comparatively low data rate. These projects are further limited to producing first principle non-elastic, low capacity integrated batteries and UHF frequency (868 MHz) RF devices which require print resolutions similar to conventional masks for wet etching (typically 200 um). Further, our experience of UHF RFID reveals transmission delays of 6 ms, and a reliable data rate upper limit of only 400 bps (corresponding to a sample rate of just 30 Hz for a modality such as accelerometry). In MultiSense, we propose to overcome these limitations by moving from RFID to 24 GHz ISM (Industrial, Scientific Medical) band transmission, where very substantial uncongested bandwidth is available, offering orders of magnitude higher bit rates than UHF. In addition, the smaller wavelengths will increase antenna miniaturisation on integrated elastic substrate batteries, requiring print resolutions of 50 um. The new batteries will be solid state and polymer based with elastic current collectors. We will also investigate the mmWave signal surface guiding over the skin as a mechanism to allow for inter-patch communications. Sensing robustness will be improved as minor variations/misplacements in the sensor positions could be captured, and potentially corrected for in software. This will impact on diagnostic EEG measurements where currently entire datasets (from cabled electrodes) might be abandoned when individual electrodes disconnect. To enable the measurement of skin-based transmission between patches with new dry electrode designs, we will work with International Research Visitor Professor Koichi Ito of Chiba university, an expert in human phantom design.

An increasing number of people live with long term physical and mental health conditions, such as diabetes, heart disease or depression. Many of these people find that their symptoms fluctuate in severity over time, including periods of relative calm and episodes during which symptoms become much worse. However, patients with long term conditions typically see their doctor during pre-arranged visits at fixed intervals, rather than on the basis of their current symptoms. For instance, people with chronic kidney disease commonly have appointments every 3 months. These visits are often felt unnecessary during stable periods, during which patients could probably manage well by themselves, but irregular enough to spot worsening symptoms early enough and prevent more severe episodes of illness - what we call 'fall back episodes'. We propose to develop a set of software tools for smartphones and tablets, called the "Wearable Clinic". This will help patients with long term conditions, together with their carers and doctors, to better manage their health in daily life, respond more quickly to changes in symptoms and prevent fall back episodes. This could prevent unplanned admissions to hospital, which are not only distressing and disruptive for patients and their families, but expensive for the NHS. Furthermore, it could make it easier to integrate care for patients with multiple long term conditions (e.g. both diabetes and chronic kidney disease), who are often treated by different doctors, at different places, and at different times. For patients, using the Wearable Clinic starts with measuring symptoms in daily life using wearables. These data are then automatically combined with data held in NHS records on their diagnoses, lab results, and treatments in order to predict the likely future course of symptoms, and whether there is a risk of a fall back episode. Finally, the software will propose a modifiable care plan that takes account of the patient's range of existing conditions, current and predicted health status, availability of local care resources, and the patient's own preferences. Where it is possible and safe to do so, care plans will remove clinically unnecessary and unwanted appointments, saving time and money for both the patient and the NHS. To achieve this vision, we propose to apply data science techniques to analyse data collected from a) medical records and b) wristband wearables and smartphone technologies ('wearables') worn by patients with long term conditions. While the Wearable Clinic concept could potentially be useful for managing a range of long term conditions, we will first test it out in two different conditions, where symptoms are known to fluctuate over time: schizophrenia and chronic kidney disease. Statistical techniques will be applied to see if data collected from patients using wearables can be used to a) predict changes in symptoms and b) produce tailored care plans for individual patients. We will trial methods that collect and use data in ways that take into account individual risk factors (e.g. age, ethnicity) and conserve the battery life of devices. While the project primarily aims to develop new computer algorithms, statistical models and computer software, we will trial the technical aspects of the Wearable Clinic with a small number of healthy volunteers, people with schizophrenia and people with chronic kidney disease. We will also investigate costs, benefits, and potential risks of the Wearable Clinic in its earliest stages of development and, where necessary and feasible, integrate solutions during the lifetime of the project. A series of workshops open to the public will be held to explore cross-cutting issues such as trustworthy data use and privacy. This will pave the way for future studies and maximise the chances that the Wearable Clinic actually makes it into practice - thus improving the quality of care for patients with long term conditions.

Dementia is often presented as a global issue with substantial economic consequences for all countries and societies providing diagnostic and/or supportive services. Whilst we believe this is necessary and important information, in our 5-year study we want to celebrate the achievements, growth and contribution that people with dementia and their carers make to society. To do this, we are putting the local neighbourhood and networks in which people with dementia and their carers live and belong at the centre of our work. We have designed a study on neighbourhood living that has 4 inter-linked work packages (WPs), an international partner , the Center for Dementia Research [CEDER] at Linköping University, Sweden, and strong user involvement through the EDUCATE and Open Doors groups [Greater Manchester, England]; The ACE Club [Rhyl, North Wales]; and the Scottish Dementia Working Group [Glasgow, Scotland]. In the UK our academic partners are situated in Manchester, Salford, Stirling, Liverpool and London and we have third sector involvement through the Deaf Heritage Project at the British Deaf Association, as well as a range of project partners which includes the North West People in Research Forum, the Citizen Scientist initiative and a Community Integrated Company that supports people with dementia through accessible technology [Finerday]. As this is a complex set of networks based around a neighbourhoods theme, each WP will use different research methods and partners to meet their primary aims and objectives. WP1 is a secondary analysis of the English Longitudinal Study of Aging database which will compile Neighbourhood Profiles that will be available for the whole country; these Profiles will include information on cognitive risk factors and clusters of population; WP2 will develop a set of core outcomes measures in dementia that will involve people with dementia and their carers in deciding what measures and priorities are important for them; WP3 will explore what makes a dementia friendly neighbourhood and will take place in Stirling, Salford and Linköping; WP4 has 3 interventions representing various stages of the Medical Research Council's complex interventions framework. Intervention 1 will be a full RCT of an educational intervention for general hospitals that several members of the project team have developed and piloted over the last 2 years. In this study, we want to find out if the educational intervention results in people with dementia leaving hospital for their neighbourhood home sooner, but with high levels of satisfaction. Interventions 2 and 3 are pilot trials. Intervention 2 will be conducted in Sweden and Manchester, UK and will use technology to help couples, where one person has a dementia, to better self-manage the condition and, more importantly, their relationship. In intervention 3, we are looking at the diversity of a neighbourhood and will develop the first digitalised life story intervention in the world for Deaf people (BSL users) who live with dementia. This will be the first intervention for this group in the world. In this programme of work we will develop a user research programme as some people with dementia have told us that they would like to work alongside the research team as co-researchers. We will therefore appoint a PPI co-ordinator for the duration of the study with a responsibility for identifying co-researcher training needs, running a regular co-research programme, mentoring co-researchers, ensuring user goal preferences are met and facilitating user dissemination. Through the implementation of a neighbourhood approach each WP will promote closer relations and working between professionals, lay people and people living with dementia. This study will also contribute to the currently limited evidence base for dementia friendly communities and provide knowledge and insights to support a robust theoretical framework of neighbourhood work that will have international scope and relevance.