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Contact the study team using the details below to take part. If there are no contact details below please ask your doctor in the first instance.
yee
mah
yee.mah@nhs.net
yee
mah
yee.mah@nhs.net
Maria Consuelo
Tibajia
mtibajia@nhs.net
Cerebrovascular diseases
This information is provided directly by researchers, and we recognise that it isn't always easy to understand. We are working with researchers to improve the accessibility of this information. In some summaries, you may come across links to external websites. These websites will have more information to help you better understand the study.
Stroke - still the second commonest cause of death and principal cause of adult neurological disability in the Western World - is characterised by rapid changes over time and marked variability in outcomes. A patient may improve or deteriorate over minutes, and the resultant disability may range from an obvious complete paralysis to subtle, task-dependent incoordination of a single limb.
Unlike many other neurological disorders, stroke can be exquisitely sensitive to prompt and intelligently tailored treatment, rewarding innovation in the delivery of care with real-world, tangible impact on patient outcomes. Optimal treatment therefore requires both detailed characterisation of the patient's clinical picture and its pattern of change over time.
Arguably the most important aspect of the patient's clinical picture -- body movement -- remains remarkably poorly documented: quantified only subjectively and at infrequent intervals in the patient's clinical evolution. The combination of artificial intelligence with high-performance computing now enables automatic extraction of a patient's skeletal frame resolved down to major joints, like that of a stick-man, to be delivered simply, safely, and inexpensively, without the use of cumbersome body worn markers. Central to this technology is patient privacy, with the skeletal frame extracted in real time, ensuring no video data, from which patients can be identified, to be stored or transmitted by the device.
Our prototype motion categorisation system -- MoCat -- will be used to study the rapid dynamics of acute stroke, seamlessly embedded in the clinical stream. By quantifying the change in motor deficit over time we shall examine the relationship between these trajectories with clinical outcomes and develop predictive models that can support clinical management and optimise service delivery.
Start dates may differ between countries and research sites. The research team are responsible for keeping the information up-to-date.
The recruitment start and end dates are as follows:
Observational type: Validation of investigation /therapeutic procedures;
You can take part if:
You may not be able to take part if:
Less than 18 years of age. No working diagnosis of acute stroke.
Below are the locations for where you can take part in the trial. Please note that not all sites may be open.
The study is sponsored by KING'S COLLEGE HOSPITAL NHS FOUNDATION TRUST and funded by Medical Research Council (MRC) .
Your feedback is important to us. It will help us improve the quality of the study information on this site. Please answer both questions.
Read full details
for Trial ID: CPMS 44886
You can print or share the study information with your GP/healthcare provider or contact the research team directly.
