In recent years, our ability to collect, store and process vast amounts of data, coupled with rapid advances in technology, have led to the widespread adoption of data-driven decision-making. This includes new application areas, such as precision medicine, where doctors are using data to inform their diagnoses and treatment recommendations. In other areas, such as finance, banks use huge amounts of historical data in order to decide whether a new customer is likely (or not) to default on their loan repayments. It is often the case that we are required to make a discrete prediction about some future patient or customer, based on some (training) data relating to existing patients. In statistics, problems of this type are called classification problems. Many methods for classification are built on the assumption that any future data we may encounter has the same distribution as our training data. Of course, this assumption is not always valid -- data relating to one set of patients or customers will not necessarily follow the same distribution as data from a new set of people. In this research, we will develop new robust classification algorithms that can deal with noisy and incomplete data. In particular, the new methodology will enable practitioners to combine multiple sources of noisy data, propose modifications to existing methods in order to guarantee they are robust to corruptions in the data, and introduce novel ways of overcoming the issues caused by missing data. We will also provide new theoretical understanding of the limitations of decision-making algorithms when faced with noisy, corrupted and incomplete data. There are a number of scenarios where our new approaches will be applicable: - We may have data collected from patients in a particular location (lab or hospital) but wish to make predictions in a different location. - We may not have access to the full dataset. For example, for privacy reasons, uses may not disclose some of their personal information. In other settings, we may be required to anonymise the data by removing some identifying covariates. - Often the complexity of the type of data involved will mean that we don't observe the true data. Instead, we only have access to an approximation of the data. This typically occurs in modern settings, where practitioners use crowd-sourcing services such as the Amazon Mechanical Turk to label their data -- such services are rarely perfectly accurate. - It may be that an adversary is able to arbitrarily contaminate a small proportion of the data (for instance by performing artificial activity online). Our work will enable practitioners to utilise data that is currently not appropriate for use. We will also provide new insight into the kinds of data that are most useful for a particular purpose.

We propose the development of a new technology for Non-Invasive Single Neuron Electrical Monitoring (NISNEM). Current non-invasive neuroimaging techniques including electroencephalography (EEG), magnetoencephalography (MEG) or functional magnetic resonance imaging (fMRI) provide indirect measures of the activity of large populations of neurons in the brain. However, it is becoming apparent that information at the single neuron level may be critical for understanding, diagnosing, and treating increasingly prevalent neurological conditions, such as stroke and dementia. Current methods to record single neuron activity are invasive - they require surgical implants. Implanted electrodes risk damage to the neural tissue and/or foreign body reaction that limit long-term stability. Understandably, this approach is not chosen by many patients; in fact, implanted electrode technologies are limited to animal preparations or tests on a handful of patients worldwide. Measuring single neuron activity non-invasively will transform how neurological conditions are diagnosed, monitored, and treated as well as pave the way for the broad adoption of neurotechnologies in healthcare. We propose the development of NISNEM by pushing frontier engineering research in electrode technology, ultra-low-noise electronics, and advanced signal processing, iteratively validated during extensive tests in pre-clinical trials. We will design and manufacture arrays of dry electrodes to be mounted on the skin with an ultra-high density of recording points. By aggressive miniaturization, we will develop microelectronics chips to record from thousands of channels with beyond state-of-art noise performance. We will devise breakthrough developments in unsupervised blind source identification of the activity of tens to hundreds of neurons from tens of thousands of recordings. This research will be supported by iterative pre-clinical studies in humans and animals, which will be essential for defining requirements and refining designs. We intend to demonstrate the feasibility of the NISNEM technology and its potential to become a routine clinical tool that transforms all aspects of healthcare. In particular, we expect it to drastically improve how neurological diseases are managed. Given that they are a massive burden and limit the quality of life of millions of patients and their families, the impact of NISNEM could be almost unprecedented. We envision the NISNEM technology to be adopted on a routine clinical basis for: 1) diagnostics (epilepsy, tremor, dementia); 2) monitoring (stroke, spinal cord injury, ageing); 3) intervention (closed-loop modulation of brain activity); 4) advancing our understanding of the nervous system (identifying pathological changes); and 5) development of neural interfaces for communication (Brain-Computer Interfaces for locked-in patients), control of (neuro)prosthetics, or replacement of a "missing sense" (e.g., auditory prosthetics). Moreover, by accurately detecting the patient's intent, this technology could be used to drive neural plasticity -the brain's ability to reorganize itself-, potentially enabling cures for currently incurable disorders such as stroke, spinal cord injury, or Parkinson's disease. NISNEM also provides the opportunity to extend treatment from the hospital to the home. For example, rehabilitation after a stroke occurs mainly in hospitals and for a limited period of time; home rehabilitation is absent. NISNEM could provide continuous rehabilitation at home through the use of therapeutic technologies. The neural engineering, neuroscience and clinical neurology communities will all greatly benefit from this radically new perspective and complementary knowledge base. NISNEM will foster a revolution in neurosciences and neurotechnology, strongly impacting these large academic communities and the clinical sector. Even more importantly, if successful, it will improve the life of millions of patients and their relatives

The Bionics+ NetworkPlus will represent the spectrum of research, clinical and industrial communities across bionic technologies within the EPSRC Grand Challenge theme of Frontiers of Physical Intervention. It will invigorate and support a cohesive, open and active network with the mission of creating a mutually supportive environment. It will lead to the co-creation of user-centred bionic solutions that are fit for purpose. These advances will have a global impact, consolidating the world-leading position of the UK. The founding tranche will focus on ambitious and transformative research, new collaborative and translational activities, and the formulation of a longer-term strategy. Within this context, as a community, we will explore and identify areas of opportunity and value, driven by Bionics users' needs, complementary to existing activity and strengths. The network will instigate and support early-stage research in these priority areas, alongside providing an outward-facing representation and engagement of the UK Bionics community. Further, we aim to contribute in an advisory capacity to public bodies, UK industry and government policy. At the time of the application, we have obtained a positive commitment from circa 70 groups including bionic users, academic partners from universities in England, Scotland, Wales and Northern Ireland and a few international partners; partners in medical devices, orthotics and prosthetics industry, both large corporates and small-medium size companies; and many clinicians, surgeons and aligned health experts from relevant NHS clinics and the private sector.

CBAS is proposing to develop a Prosthetic Interface Device (PID): Digital, an innovative, continuous, system that aids in collecting data remotely for patients with mobility impairments: patients with lower limb disorders and vulnerable elderly people. PID: Digital takes advantage of the CBAS machine-learning (ML) platform. This system is used to collect healthcare data from sensors worn by patients to enable remote assessment of their health. It provides clinicians with a true and complete picture of activity and mobility by representing patient conditions. This offers clinicians a clear tool to see that treatment is effective, progression of disease, and even clinical key performance indicators (treatment adherence/compliance measures). PID: Digital, can predict the need for in-house consultations with clinicians, potentially alleviating dependence on direct interaction between healthcare provider and patient, and supporting patient autonomy. The benefits include continuity of care, condition specific data, proactive intervention and reduced face-to-face assessment time via targeted patient engagement. This study will optimise existing ML algorithms for implementation in a cloud environment and build a system to scale these across multiple patients, clinicians and data types. These algorithms will provide clinically important information for identified patient groups, accessed via client end dashboards. All patients will be assessed in QMUL Gait Analysis Laboratory, providing gold standard validation. Clinical studies carried out by collaborators CUSH Health Ltd and Andiamo will trial PID: Digital alongside current best practise assessment methods. A regulatory and ethically compliant cloud environment and associated data storage will be designed and built with dashboards for identified for specified patients and associated user groups. The resulting system will be compliant to all medical device regulatory requirements to enable remote patient health assessment. On project completion, PID: Digital will have been trialled with two patient groups and be ready for regulatory submission as a class 1M medical device