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Artificiell intelligens och intelligenta system

Automatiserade mjukvaruspråkutveckling och mjukvaruteknik

Certifierbara bevis och justifieringsteknik

Cyber-fysisk systemanalys

Datakommunikation

Digitalisering

Formell modellering och analys av inbyggda system

Förnybar energi

Industriell programvaruteknik

Komplexa inbyggda system i realtid

Lärande och optimering

Medicinsk teknik

Modellbaserad konstruktion av inbäddade system

Programmeringsspråk

Programvarutestlaboratorium

Resurseffektivisering

Säkerhetskritisk teknik

Teknisk matematik

Heterogena system

Innocare

The project aims to make a survey for products used for home care and to analyze what kind of equipment wanted by the users, staff and Nacka Municipality.

Avslutat

Start

2012-01-01

Avslut

2012-08-30

Forskningsområde

Forskningsinriktning

Projektansvarig vid MDH

No partial template found

Description of the project

Stroke is affecting around 30 000 people in Sweden every year. Despite intensive rehabilitation, a large group continues to live with persistent disabilities. Physical rehabilitation consists of regular training with a physiotherapist to increase mobility and strength of the affected limb. During training, patients are encouraged to simultaneously imagine the trained movements. Despite a lot of research showing the importance of MI for physical recovery, a means to measure MI in real time does not yet exist.

This is where IEMI comes in. The extracted measure of mental imagery will serve as crucial decision support for: 1) physiotherapists, who will receive real-time information on the mental engagement of patients during rehabilitation, 2) stroke patients, who will receive real-time feedback to strengthen their MI and directly enhance related brain activations. In addition, collected brain activity data will serve as a basis for developing functional diagnostics tools that can serve as a support for assessing the severity of stroke and deciding appropriate strategy for rehabilitation.

The outcomes of IEMI are expected to yield a prototype system of clinical decision support for enhanced stroke rehabilitation. Expert competences in artificial intelligence and in specialized stroke rehabilitation are merged in IEMI to present a highly innovative technology with the potential to substantially increase the quality of stroke rehabilitation.

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