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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

Artificiell intelligens och intelligenta system

Automatiserade mjukvaruspråkutveckling och mjukvaruteknik

Certifierbara bevis och justifieringsteknik

IEMI: Intelligent extraction of mental imagery during stroke rehabilitation

IEMI aims to develop intelligent algorithms for extracting a continuous measure, from the brain activity, related to Mental Imagery (MI) of a physical movement. The project specifically targets stroke survivors in their rehabilitation process towards physical recovery.

Avslutat

Start

2017-07-01

Avslut

2019-06-30

Huvudfinansiering

Samarbetspartners

Forskningsområde

Forskningsinriktning

Projektansvarig vid MDH

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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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