ETH Summer School on Soft Robotics
Thank you very much for joining SSSR 2012 in Zurich! Recap
1st Swiss rtfMRI Neurofeedback Conference
A big thank you to all the speakers and over 150 participants! Recap
Kaspar Leuenberger presents his PhD project at the ETH Tag.
We welcome Frieder Wittman who joins us from the Technical University Munich.
Mike Rinderknecht receives the 1st prize in the IEEE Region 8 Student Paper Contest
2012 Swiss Brain League research prize awarded to Silvio Ionta and Lukas Heydrich for work in collaboration with RELab.
RELab present at the BrainFair Zurich 2012.
RELab hosts the 1st Swiss rtfMRI Neurofeedback Conference with over 150 participants.
We welcome Auralius Manurung, who joins us from the Gyeongsang National University, South Korea.
Marie-Christine Fluet receives the Best Poster Award at the 2011 International Conference on Rehabilitation Robotics (ICORR 2011)
RELab featured on ETH Life
Stroke patients receive intensive therapy in rehabilitation centers. Once they are at home, no objective information of their activity is available. Especially the tasks they perform with the affected upper limbs are mainly unknown. There is no good mean to compare the patient’s behavior in all-day life before and after intensive therapy sessions.
In this project we develop small sensor modules and algorithms to record and classify the patient’s activities over long periods. To guarantee an operating time of several weeks required for this project, we develop our proper hardware. The processor and sensors can be selected suiting exactly our application and total hardware access enables for controlling the power consumption of the components during operation.
These modules will later be used to study upper limb movements over longer periods. Algorithms to classify these movements have to be researched.
A small low-power sensor module has been developed and is currently evaluated. First tests have shown promising results regarding low power consumption and precision of the measurements.
The hardware and algorithms developed within this project can be powerful tools for assessment and show the doctors how therapies transfer to each subject.
Rehabilitation Engineering Lab
ML G 53.1
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