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Automating the Fugl-Meyer Assessment with Supervised Machine Learning

Title
Automating the Fugl-Meyer Assessment with Supervised Machine Learning
Translated Title
Supervised 기계학습을 이용한 Fugl-Meyer 평가의 자동화
Authors
Paul Otten
DGIST Authors
Paul Otten; Son, Sang Hyuk; Kim, Jong Hyun
Advisor(s)
Son, Sang Hyuk
Co-Advisor(s)
Kim, Jong Hyun
Issue Date
2015
Available Date
2015-01-12
Degree Date
2015. 2
Type
Thesis
Keywords
stroke assessmentFugl-Meyer Assessmentmachine learningSupport Vector MachineKinectMachine learningstroke evaluationkinectCPS
Abstract
Evaluation of post-stroke hemiplegic patients is an important aspect of rehabilitation, especially for assessing improvement of a patient’s condition from a treatment. It is also a necessary step to perform during clinical trials. The Fugl-Meyer Assessment (FMA) is one of the most widely recognized and utilized measures of body function impairment for post-stroke patients. We propose a method for automating the upper-limb portion of the FMA by gathering data from sensors monitoring the patient. Features are extracted from the data and processed by a machine learning algorithm. The output from the machine learning algorithm returns a value that can be used to score a patient’s upper limb functionality. The machine learning algorithms tested in our system were Support Vector Machines (SVM) and Backpropagation Neural Networks (BNN). This system will enable automating and inexpensive stroke patient evaluation that can save up to 30 minutes per patient for a doctor, providing a time-saving service for doctors and stroke researchers. ⓒ 2015 DGIST
Table Of Contents
I.INTRODUCTION 1 -- II. RELATED WORKS 7 -- III. SENSOR DATA 10 -- 3.1 Kinect 11 -- 3.2 Wired Glov 12 -- 3.3 Pressure Sensor 12 -- 3.4 Additional Sensors 13 -- 3.5 Data Aggregation 15 -- IV. MACHINE LEARNING 16 -- 4.1 SVM Overview 16 -- 4.2 BNN Overview 18 -- 4.3 Feature Extraction 19 -- 4.4 Training SVM 24 -- 4.5 Training BNN 27 -- V. EXPERIMENTAL RESULTS 30 -- VI. CONCLUSION 32 -- References 33 -- Korean Summary 36 -- Acknowledgements 37 -- Curriculum Vitae 38
URI
http://dgist.dcollection.net/jsp/common/DcLoOrgPer.jsp?sItemId=000001922800
http://hdl.handle.net/20.500.11750/1403
DOI
10.22677/thesis.1922800
Degree
Master
Department
Information and Communication Engineering
University
DGIST
Files:
Collection:
Information and Communication EngineeringThesesMaster


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