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。

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.

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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