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Non-lineal EEG Modelling by Using Quadratic Entropy for Arousal Level Classification Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Quantitative EEG and Virtual Reality to Support Post-stroke Rehabilitation at Home

Authors : Alfonso Mastropietro, Sara Arlati, Simona Mrakic-Sposta, Luca Fontana, Cristina Franchin, Matteo Malosio, Simone Pittaccio, Cristina Gramigna, Franco Molteni, Marco Sacco, Giovanna Rizzo

Published in: Innovation in Medicine and Healthcare 2016

Publisher: Springer International Publishing

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Abstract

Post-stroke rehabilitation has an enormous impact on health services worldwide because of the high prevalence of stroke, in continuous growth due to the progressive population aging. Systems for neuro-motor rehabilitation at home can help reduce the economic burden of long lasting treatment in chronic post-stroke patients; however the efficacy of these systems in providing a correct and effective rehabilitation should be established. From this point of view, coupling home rehabilitation systems with quantitative EEG methodologies for objectively characterizing patients’ cerebral activity could be useful for the clinician to optimize the rehabilitation protocol and assess its efficacy. Moreover, the use of virtual/augmented reality technologies could assist the patients during unsupervised rehabilitation by providing an empathic feedback to improve their adherence to the treatment. These two aspects were studied and implemented in RIPRENDO@home, a multidisciplinary project, aimed to develop an integrated technological platform oriented to home neurorehabilitation for stroke patients.

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Metadata
Title
Quantitative EEG and Virtual Reality to Support Post-stroke Rehabilitation at Home
Authors
Alfonso Mastropietro
Sara Arlati
Simona Mrakic-Sposta
Luca Fontana
Cristina Franchin
Matteo Malosio
Simone Pittaccio
Cristina Gramigna
Franco Molteni
Marco Sacco
Giovanna Rizzo
Copyright Year
2016
Book
Innovation in Medicine and Healthcare 2016

Print ISBN: 978-3-319-39686-6

Electronic ISBN: 978-3-319-39687-3

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-39687-3_15

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