||A Cyber-Physical System for Healthcare and Rehabilitation: Pelvic Floor Muscle Training
||Department of Computer Science and Information Engineering
Urinary Incontinence Treatment
Pelvic Floor Muscle Training
|| 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
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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