Formell modellering och analys av inbyggda system
Artificiell intelligens och intelligenta system
Automatiserade mjukvaruspråkutveckling och mjukvaruteknik
Certifierbara bevis och justifieringsteknik
Komplexa inbyggda system i realtid
Lärande och optimering
Modellbaserad konstruktion av inbäddade system
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.
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.