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Machine Learning in DigitalTelerehabilitation: Telerehabilitation system based on kinect
Linnaeus University, Faculty of Technology, Department of Computer Science.
2015 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesis
Abstract [en]

The healthcare as a service is always under pressure and is in great demand. Despiteliving in a developed world with access to cars, trains, busses and other transportationmeans, sometimes accessing healthcare can be troublesome and costly. Thecontinuous technological progress provides new means to provide different kind ofservices, healthcare included. One way of putting technology into good use in fieldof healthcare is remote rehabilitation.Remote rehabilitation is a matter of delivering physiotherapy on a distance. Theuse of remote rehabilitation potentially reduces waiting time for treatment and gives apossibility for people with long traveling distance, to be treated at their locations. Thethesis addresses a solution to physiotherapy on distance that utilizes Kinect and machinelearning technologies to provide physiotherapy offline. Thesis presents KinectDigital Rehabilitation Assistant (KiDiRA), which provides simple functions to sufficethe needs of a physiotherapist to plan therapeutical treatment and the ability of apatient to get access physiotherapy offline in real-time at home.More precisely KiDiRA is the system that combines Kinect motion capture device,an interactive graphical interface and a platform to assist with the design ofphysiotherapeutical exercises and an aid for the patient to execute therapeutic plan onhis/her own. The system displays the exercise directives and monitors performanceof patient. KiDiRA aims to incorporate science of machine-learning in process ofperformance evaluation during exercises.

Place, publisher, year, edition, pages
2015. , p. 71
Keywords [en]
machine learning, remote rehabilitation, kinect
National Category
Human Computer Interaction
Identifiers
URN: urn:nbn:se:lnu:diva-70048OAI: oai:DiVA.org:lnu-70048DiVA, id: diva2:1177240
Subject / course
Computer Science
Educational program
Software Technology Programme, 180 credits
Supervisors
Examiners
Available from: 2018-01-25 Created: 2018-01-24 Last updated: 2018-01-25Bibliographically approved

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CiteExportLink to record
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  • apa
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