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Novel machine learning techniques to improve the forecasting of stroke post-interventive outcomes

Project description

Algorithms for effective stroke rehabilitation

In 2015 alone, 600 000 strokes occurred in Europe. As a top-level health problem in Europe, stroke requires strict rehabilitation. Unfortunately, there is currently a lack of methods that effectively measure patient progress and results in treatment. The EU-funded MAESTRO project will develop algorithms enabling evaluation of rehabilitation effectiveness and its optimisation by using wearables (mobile applications and IoT devices). The innovation is novelty machine learning techniques (deep learning), enabling the automated classification of particularly complex data, and a pioneering extraction of vital information from data sets to provide doctors, patients and caregivers with group-specific levels of feedback. MAESTRO is entirely in alignment with the Horizon 2020 goals of Area III – digitisation, research and innovation.


Stroke is a first-order medical problem (about 600,000 strokes occurred in the EU in 2015), in which rehabilitation is critical. Currently, there are no reliable systems to monitor the patient adherence to this rehabilitation, nor its effectiveness. Combining the ER experience on biosensors and gamification, the expertise on outlier detection and machine learning of IMDEA Networks, and the knowledge on deep learning applied to medicine of the AI Lab at Brown University, in MAESTRO, we will develop algorithms capable of determining rehabilitation adherence and effectiveness by using wearables. This will optimize rehabilitation and forecast recovery by providing information to neurologists and feedback to patients and caregivers. MAESTRO aligns with the H2020 goals in Area III (digitization, research and innovation) as well as health, demographic change, and wellbeing.
MAESTRO aims at recruiting 50 patients from Rhode Island Hospital for 4 months in the first of three development cycles. Mobile applications, IoT devices and questionnaires will be used in the first of the three cycles. This is viable since we will use the infrastructure and connections of an existing stroke project on-site.
The innovation in MAESTRO lays in the development of software solutions to monitor the rehabilitation of post-stroke patients remotely and passively using off-the-shelf hardware and gamification. The methods employed in MAESTRO, particularly deep learning, permit the automated classification of extremely complex data, allowing scientists to extract important information from data sets that would be unmanageable otherwise.
MAESTRO is a unique scientific advance because it will provide doctors, patients and caregivers, group-specific levels of feedback. In addition, the algorithms specifically developed within the project can be the bases of novel developments with different goals, for example translation to clinical practice, or expansion to other neurodegenerative diseases.


Net EU contribution
€ 245 732,16
Avenida del mar mediterraneo 22
28918 Leganes (madrid)

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Comunidad de Madrid Comunidad de Madrid Madrid
Activity type
Research Organisations
Other funding
€ 0,00

Partners (1)