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系統識別號 U0002-1607201217120600
中文論文名稱 健康照護與醫療復健的信息物理系統:骨盆底肌肉訓練
英文論文名稱 A Cyber-Physical System for Healthcare and Rehabilitation: Pelvic Floor Muscle Training
校院名稱 淡江大學
系所名稱(中) 資訊工程學系博士班
系所名稱(英) Department of Computer Science and Information Engineering
學年度 100
學期 2
出版年 101
研究生中文姓名 蔡坤孝
研究生英文姓名 Kun-Hsiao Tsai
學號 897410147
學位類別 博士
語文別 英文
口試日期 2012-07-04
論文頁數 82頁
口試委員 指導教授-陳瑞發
委員-白文章
委員-王亦凡
委員-方鄒昭聰
委員-葛煥昭
委員-王英宏
委員-陳瑞發
中文關鍵字 健康照護  醫療復健  信息物理系統  尿失禁治療  骨盆底肌肉訓練  凱格爾運動 
英文關鍵字 Healthcare  Rehabilitation  Cyber-Physical Systems  Urinary Incontinence Treatment  Pelvic Floor Muscle Training  Kegel Exercise 
學科別分類 學科別應用科學資訊工程
中文摘要 近年來,健康照護與醫療復健逐漸受到重視,加上科技日新月異,許多傳統的醫療設備在結合新的電子技術後,使其產生了更強大的功能。在醫療復健上,因為許多的醫療設備尚未與電子技術結合,使得醫師無法隨時掌握病人復健的情況,而病人在家裡進行復健時,因為沒有醫療人員的協助下,常無法得知自己的動作是否正確,進而影響復健的成效。
在本篇論文中,我們提出一新的醫療架構,將傳統的醫療復健設備與電子技術結合,再透過資料的蒐集、量化、分群、分類與分析等步驟,來協助醫生進行病人的復健,其中,經由病人復健資料的分群,我們學習了病人的復健動作,再透過分類的過程,來比對病人的復健動作是否正確,以幫助病人自行在家中進行復健。
實作上,我們將此架構應用於醫學上的骨盆底肌肉訓練,我們與台北榮總婦產科的醫師合作,將系統實際應用於尿失禁的患者上,並用實驗的數據,來證明系統能有效的協助病人進行復健,進而改善他們的復健療程。
英文摘要 Recently, healthcare and rehabilitation are becoming more and more important. Due to the advancement of technology, traditional medical equipment may produce great contributions after the combination of equipment and electronic technology. Nowadays, much medical equipment is not combined with electronic technology. Hence, doctors do not track patients’ courses of treatment. Moreover, when patients do rehabilitation exercises at home without the assistance of doctors or nurses, they cannot confirm that their rehabilitation motions are correct or not. It may affect the effectiveness of rehabilitation.
In this dissertation, we propose a new rehabilitation architecture. We connect traditional medical equipment with electronic technology. We assist doctors in understanding patients’ rehabilitation via the process of data collection, quantization, clustering, classification, and analysis. Also, by the process of clustering and classification, patients can determine whether their rehabilitation actions are correct or not and adjust their own rehabilitation actions accordingly to improve the effectiveness of rehabilitation.
In implementation, we apply our architecture to pelvic floor muscle training (PFMT). We cooperated with a doctor of the Department of Obstetrics and Gynecology, Taipei Veterans General Hospital, Taiwan, and performed a series of experiments to verify our theory. The system has been implemented in medicine and has been used to treat patients with urinary incontinence in practice. We hope that this system can help more patients in the future.
論文目次 Chapter 1 Introduction 1
Chapter 2 Related Work 3
2.1 Healthcare and Rehabilitation 3
2.1.1 Healthcare 3
2.1.2 Rehabilitation 4
2.2 Urinary Incontinence 6
2.2.1 Electrical Stimulation 6
2.2.2 Pelvic Floor Muscle Training 7
2.3 Cyber-Physical Systems 8
2.4 Data Analysis 10
Chapter 3 Rehabilitation Cyber-Physical System 12
3.1 System Overview 12
3.1.1 Pre-Treatment 13
3.1.2 Intra-Treatment 13
3.1.3 Post-Treatment 14
3.2 System Architecture 14
3.2.1 Physical Layer 15
3.2.2 Sensing Layer 17
3.2.3 Network Layer 19
3.2.4 Application Layer 20
Chapter 4 Rehabilitation Cyber-Physical System for Pelvic Floor Muscle Training 28
4.1 Physical Layer of PFMT 32
4.2 Sensing Layer of PFMT 33
4.2.1 Data Collection 37
4.3 Network Layer of PFMT 42
4.4 Application Layer of PFMT 43
4.4.1 Data Analysis 44
Chapter 5 Experimental Results 57
5.1 PFMT Rehabilitation in the Standing Position 57
5.1.1 Hardware Design 57
5.1.2 Rehabilitation Procedure 60
5.2 PFMT Rehabilitation in the Lying Position 62
5.2.1 Hardware Design 62
5.2.2 Rehabilitation Procedure 64
5.3 Clinical Trial 67
Chapter 6 Conclusions and Future Work 70
6.1 Conclusions 70
6.2 Future Work 71
Bibliography 72

List of Figures
Fig. 2.1 Wireless Body Area Network for Patient Monitoring [Jovanov 05] 4
Fig. 2.2 Concept of home rehabilitation [Kohler 10] 5
Fig. 2.3 Sensor design and example [Kohler 10] 5
Fig. 2.4 A Novel Wireless Electrical Muscle Simulator Equipment [Wang 09] 7
Fig. 2.5 An illustrative example of traffic control system [Kim 12] 9
Fig. 3.1 System overall 12
Fig. 3.2 System architecture 15
Fig. 3.3 An example for stroke equipment (Source: [Giansanti 08]) 16
Fig. 3.4 An example for wrist equipment (Source: [Takaiwa 10]) 16
Fig. 3.5 An example for force sensors 17
Fig. 3.6 An example for sonar sensors 17
Fig. 3.7 An example of data collection 18
Fig. 3.8 An example of ZigBee wireless network 19
Fig. 3.9 An example of smartphone networks 20
Fig. 3.10 An example of quantization 22
Fig. 3.11 An example of clustering 24
Fig. 3.12 An example of classification 26
Fig. 3.13 An example of analysis 27
Fig. 4.1 Our Architecture of PFMT 28
Fig. 4.2 The PFMT flowchart of home rehabilitation 30
Fig. 4.3 The relationship between the RCPS model and PFMT 31
Fig. 4.4 The medical equipment of PFMT in the standing position 32
Fig. 4.5 The medical equipment of PFMT in the lying position 33
Fig. 4.6 The relationship between an accelerometer sensor and the gravity of Earth 35
Fig. 4.7 An embedded system 36
Fig. 4.8 Signals of an incorrect PFMT exercise in the standing position 37
Fig. 4.9 Signals of a correct PFMT exercise in the standing position 37
Fig. 4.10 Signals of a PFMT exercise in the lying position 38
Fig. 4.11 The state diagram (NFA) of PFMT in the standing position 39
Fig. 4.12 The state diagram (NFA) of PFMT in the lying position 40
Fig. 4.13 Successful patterns and failing patterns 41
Fig. 4.14 A series of patterns in a full PFMT exercise 41
Fig. 4.15 Network layer architecture of PFMT 42
Fig. 4.16 Home rehabilitation architecture 43
Fig. 4.17 The intelligent medical treatment system between home and hospital 44
Fig. 4.18 Original force and quantized force of five pelvic floor muscle contraction motions (PFMT in the standing position) 45
Fig. 4.19 Quantized force and the standard deviations of force during a PFMT exercise in the standing position 45
Fig. 4.20 The relationship of duration and quantized force of pelvic floor muscle contraction motions during a PFMT exercise in the standing position 46
Fig. 4.21 The standard deviation of pelvic floor muscle contraction motions during a PFMT exercise in the standing position 47
Fig. 4.22 Original angle and quantized angle of five pelvic floor muscle contraction motions (PFMT in the lying position) 48
Fig. 4.23 Quantized angle and the standard deviations of angle during a PFMT exercise in the lying position 48
Fig. 4.24 The relationship of duration and quantized angle of pelvic floor muscle contraction motions during a PFMT exercise in the lying position 49
Fig. 4.25 The standard deviation of pelvic floor muscle contraction motions during a PFMT exercise in the lying position 50
Fig. 4.26 The clustering result during a PFMT exercise in the standing position 52
Fig. 4.27 The clustering result during a PFMT exercise in the standing position (P1) 54
Fig. 4.28 The clustering result during a PFMT exercise in the standing position (P2) 54
Fig. 4.29 A rehabilitation data in 15 days 55
Fig. 4.30 A rehabilitation data after 6 months 56
Fig. 5.1 The device hardware of PFMT in the standing position 58
Fig. 5.2 The device of PFMT in the standing position 59
Fig. 5.3 A demo of PFMT in the standing position 59
Fig. 5.4 Data collection of PFMT in the standing position 60
Fig. 5.5 Quantization of PFMT in the standing position 60
Fig. 5.6 Clustering of PFMT in the standing position 61
Fig. 5.7 Classification of PFMT in the standing position 61
Fig. 5.8 Analysis of PFMT in the standing position 61
Fig. 5.9 The device hardware of PFMT in the lying position 62
Fig. 5.10 The device of PFMT in the lying position 64
Fig. 5.11 A demo of PFMT in the lying position 64
Fig. 5.12 Data collection of PFMT in the lying position 65
Fig. 5.13 Quantization of PFMT in the lying position 65
Fig. 5.14 Clustering of PFMT in the lying position 66
Fig. 5.15 Classification of PFMT in the lying position 66
Fig. 5.16 Analysis of PFMT in the lying position 66

List of Tables
Table 4.1 PFMT Sensors 33
Table 5.1 The device specification of PFMT in the standing position 58
Table 5.2 The device specification of PFMT in the lying position 63
Table 5.3 The RCPS accuracy of PFMT in the standing position 67
Table 5.4 The RCPS accuracy of PFMT in the lying position 67
Table 5.5 The motion accuracy of PFMT in the standing position with the RCPS 68
Table 5.6 The motion accuracy of PFMT in the standing position without the RCPS 69
Table 5.7 The motion accuracy of PFMT in the lying position with the RCPS 69
Table 5.8 The motion accuracy of PFMT in the lying position without the RCPS 69
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