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Development of rehabilitation devices with artificial intelligence for Gait Disorder symptom of Parkinson's Disease
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 고정혁 | - |
| dc.contributor.author | 김현종 | - |
| dc.date.accessioned | 2024-01-03T17:28:35Z | - |
| dc.date.available | 2024-01-03T17:28:35Z | - |
| dc.date.created | 2023-03-03 | - |
| dc.date.issued | 2023 | - |
| dc.identifier.uri | http://repository.kmou.ac.kr/handle/2014.oak/13115 | - |
| dc.identifier.uri | http://kmou.dcollection.net/common/orgView/200000665221 | - |
| dc.description.abstract | Parkinson’s Disease(PD) is one of neurodegenerative disease. Perfect cure methods have still not been found yet. Medication and rehabilitation methods are utilized to control severity. Cueing and feedback training, are utilized to improve a patient’s quality of life and prevent rapid degradation speed. Devices that detect and classifies a user’s gait based on several factors is required to use those rehabilitation methods. Therefore, in this research, I developed gait detection and classification devices. After, I also developed adaptive control algorithm for inertial measurement unit devices. At first, the simple and mechanical gait detection and classification system showed that they could detect a user’s gait based on a metal bead and a microswitch. Secondly, the advanced Gait Detection and Classification (GDC) devices and GDC algorithm using acceleration and angular velocity from IMU were designed. Finally, the adaptive Gait Detection and Classification Rate (GDCR) control algorithm was developed after statistically analyzing results. In this research, I could observe the designed devices could detect and classify users’ gait based on various factors. I am looking forward to using this device in the future to use effective rehabilitation methods. | - |
| dc.description.tableofcontents | 1. Introduction 1 1.1 Parkinson’s disease 1 1.2 Rehabilitation methods 2 1.3 Objective and organization 3 2. Simple and mechanical augmented feedback training devices for Parkinson’s Disease : A pilot study (DOI : 10.5916/jamet.2022.46.2.64) 5 2.1 Introduction 5 2.2 Device design and experimental sequences 6 2.2.1 Mechanism for walking detection 6 2.2.2 Design and fabrication 12 2.2.3 Experimental setup 16 2.2.3.1 Pre-experiments 16 2.2.3.2 Device performance test 18 2.2.3.3 Pre-experiments for the possibility of gait symptom rehabilitation 20 2.3 Results 20 2.3.1 Mechanism validation 20 2.3.2 Pre-experiment 21 2.3.3 Device performance experiment 22 2.3.4 Pre-experiments for the possibility of gait symptoms rehabilitation 23 2.4 Discussion & conclusion 23 3. Gait disorder detection and classification method using inertia measurement unit for augmented feedback training in wearable devices (DOI : 10.3390/s21227676) 28 3.1 Introduction 28 3.2 Materials and methods 30 3.2.1 Numerical modeling 30 3.2.1.1 Joint angle converting 30 3.2.1.2 Evaluation algorithm 32 3.2.2 Gait characteristic checking with numerical modeling 34 3.2.2.1 Joint angle converting feasibility test 34 3.2.2.2 Gait speed effect test 35 3.2.2.3 Gait disorder effect test 35 3.2.2.4 Actual GDC test 35 3.3 Results 36 3.3.1 Joint angle converting 36 3.3.2 Gait speed effect test 37 3.3.3 Gait disorder effect test 38 3.3.4 Actual GDC test 38 3.4 Discussion 40 3.5 Conclusion 43 4. Adaptive control method to control gait detection and classification method for feedback training devices with inertial measurement unit 46 4.1 Introduction 46 4.2 Materials and methods 48 4.2.1 Summary of the previous research 48 4.2.2 Experiments for data collection 50 4.2.3 Impact of the AT and GT on the GDCR 51 4.2.4 Algorithm 52 4.3 Results 57 4.3.1 Statistical analysis results of the effect of the AT and GT on the GDCR 57 4.3.2 Algorithm evaluation 57 4.4 Discussion and conclusion 61 5. Conclusion 63 5.1 Research conclusion 63 5.2 Expected applications 63 6. Side works 65 6.1 Journal papers 65 6.2 Patents 65 6.3 Conferences 66 6.4 Books 67 6.5 Research experiences 67 6.6 Award and honor 68 7. References 69 8. Appendix 80 | - |
| dc.language | eng | - |
| dc.publisher | 한국해양대학교 대학원 | - |
| dc.rights | 한국해양대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | Development of rehabilitation devices with artificial intelligence for Gait Disorder symptom of Parkinson's Disease | - |
| dc.type | Dissertation | - |
| dc.date.awarded | 2023-02 | - |
| dc.embargo.terms | 2023-03-03 | - |
| dc.contributor.alternativeName | Hyeonjong Kim | - |
| dc.contributor.department | 대학원 기계공학과 | - |
| dc.contributor.affiliation | 한국해양대학교 대학원 기계공학부 | - |
| dc.description.degree | Master | - |
| dc.identifier.bibliographicCitation | 김현종. (2023). Development of rehabilitation devices with artificial intelligence for Gait Disorder symptom of Parkinson’s Disease. | - |
| dc.subject.keyword | Parkinson’s disease, Rehabilitation, Gait detection, Gait classification, Adaptive control | - |
| dc.identifier.holdings | 000000001979▲200000003272▲200000665221▲ | - |
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