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Clinically Relevant Automatic Assessment of Upper-limb Motor Function Impairment

Title
Clinically Relevant Automatic Assessment of Upper-limb Motor Function Impairment
Authors
Seunghee Lee
DGIST Authors
Lee, Seunghee; Lee, Yang-soo; Kim, Jonghyun
Advisor(s)
김종현
Co-Advisor(s)
Yang-soo Lee
Issue Date
2020
Available Date
2020-06-23
Degree Date
2020-02
Type
Thesis
Description
Neurorehabilitation, Fugl-Meyer Assessment, Automatic assessment, Artificial intelligence
Abstract
Assessment and evaluation of motor function in stroke patients is important in the neurorehabilitation interventions. In this paper, I proposed a system for automatically evaluating the motor function of stroke patients through the sensor fusion (depth camera sensor and force sensing resistor) and algorithm implementation. This research aims to overcome the limitations of conventional FMA and existing previous automated FMA related studies. In particular, two different algorithms (rule-based binary logic algorithm and fuzzy logic algorithm) were proposed to verify the feasibility and applicability of the proposed automated FMA system. Clinical trials with 51 stroke patients were performed for the system validation. The proposed system shows high FMA score classification accuracy (more than 90% agreement) through rule-based binary logic algorithm. The calculated continuous FMA also showed a high correlation (Pearson's correlation r = 0.923). The proposed automated FMA system can be applied to robot-aided therapy and remote / home rehabilitation in near future.
Table Of Contents
I. Introduction 1.1 Limitations of FMA 1.2 Related Works on automated FMA 1.3 Underlying philosophy of FMA 1.4 Considerations for implementing automated FMA II. Proposed automated FMA system 2.1 Target FMA tests and sensor selection 2.2 Feature selection based on FMA guideline 2.3 Feature extraction 2.4 Rule-based binary logic classification 2.5 Fuzzy logic FMA score calculation 2.5.1 Fuzzy variables 2.5.2 Fuzzy rules 2.5.3 Fuzzy inference system 2.6 System configurations III. Clinical experiment 3.1 Experimental setup 3.2 Participant 3.3 Protocol 3.4 Data analysis IV. Result 4.1 Time efficiency 4.2 Evaluation of rule-based binary logic algorithm 4.2.1 Classification accuracy 4.2.2 Total FM score accuracy 4.2.3 Clinical outcome comparison 4.3 Evaluation of fuzzy logic algorithm 4.3.1 Continuous FM scale score comparison 4.3.2 Total score comparison 4.3.3 7-point scaled FM score comparison 4.3.4 Clinical outcome comparison V. Discussion VI. Conclusion
URI
http://dgist.dcollection.net/common/orgView/200000284095
http://hdl.handle.net/20.500.11750/11980
DOI
10.22677/Theses.200000284095
Degree
Doctor
Department
Robotics Engineering
University
DGIST
Files:
There are no files associated with this item.
Collection:
Department of Robotics EngineeringThesesPh.D.


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