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Control System Design for Electrical Stimulation in Upper Limb Rehabilitation: Modelling, Identification and Robust Performance 1st ed. 2016 [Kõva köide]

  • Formaat: Hardback, 176 pages, kõrgus x laius: 235x155 mm, kaal: 4144 g, 23 Tables, color; XIII, 176 p., 1 Hardback
  • Ilmumisaeg: 04-Nov-2015
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319257048
  • ISBN-13: 9783319257044
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  • Formaat: Hardback, 176 pages, kõrgus x laius: 235x155 mm, kaal: 4144 g, 23 Tables, color; XIII, 176 p., 1 Hardback
  • Ilmumisaeg: 04-Nov-2015
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319257048
  • ISBN-13: 9783319257044

This book presents a comprehensive framework for model-based electrical stimulation (ES) controller design, covering the whole process needed to develop a system for helping people with physical impairments perform functional upper limb tasks such as eating, grasping and manipulating objects.

The book first demonstrates procedures for modelling and identifying biomechanical models of the response of ES, covering a wide variety of aspects including mechanical support structures, kinematics, electrode placement, tasks, and sensor locations. It then goes on to demonstrate how complex functional activities of daily living can be captured in the form of optimisation problems, and extends ES control design to address this case. It then lays out a design methodology, stability conditions, and robust performance criteria that enable control schemes to be developed systematically and transparently, ensuring that they can operate effectively in the presence of realistic modelling uncertainty, physiological variation and measurement noise.

1 Introduction
1(6)
1.1 Rehabilitation Technologies
1(2)
1.2 Role of Control Systems
3(1)
1.3 Book Structure
4(3)
References
5(2)
2 Modeling and Identification
7(14)
2.1 Modeling of the Mechanically Supported Human Arm
7(5)
2.1.1 Human Arm Dynamics
8(1)
2.1.2 Muscle Selection and Modeling
8(1)
2.1.3 Mechanical Support
9(1)
2.1.4 Combined Dynamics
10(2)
2.2 Model Identification
12(7)
2.2.1 Muscle Axis Identification
12(1)
2.2.2 Passive Parameter Identification
13(1)
2.2.3 Muscle Identification
14(1)
2.2.4 Multiplicative Muscle Function Identification
15(1)
2.2.5 Case Study: Triceps and Anterior Deltoid with ArmeoSpring
16(3)
2.3 Conclusions
19(2)
References
19(2)
3 Feedback Control Design
21(24)
3.1 General Feedback Control Framework
22(5)
3.1.1 Stability of Unactuated Joints
23(4)
3.2 Case Study: Input-Output Linearizing Controller
27(4)
3.2.1 Optimal Tracking Controller
30(1)
3.3 Robust Performance
31(9)
3.4 Case Study: Proportional-Integral-Derivative Controller
40(1)
3.5 Conclusions
41(4)
References
42(3)
4 Iterative Learning Control Design
45(30)
4.1 General ILC Framework
46(7)
4.2 Case Study: ILC Applied to Input-Output Linearized System
53(3)
4.2.1 Test Procedure
54(1)
4.2.2 Experimental Results
55(1)
4.3 Case Study: ILC Applied to Non-linearized System
56(3)
4.3.1 Experimental Results
57(2)
4.4 Robust Performance
59(13)
4.5 Conclusions
72(3)
References
72(3)
5 Clinical Application: Multiple Sclerosis
75(10)
5.1 System Description and Set-Up
75(3)
5.1.1 Outcome Measures
77(1)
5.2 Results
78(3)
5.2.1 Assisted Tracking Performance
78(2)
5.2.2 Unassisted Tracking Performance
80(1)
5.2.3 Clinical Outcome Measures
81(1)
5.3 Discussion
81(2)
5.4 Conclusions
83(2)
References
83(2)
6 Constrained ILC for Human Motor Control
85(26)
6.1 Extended Task Representation
85(2)
6.2 Reduced Stimulation and Joint Subspaces
87(1)
6.3 Extended ILC Framework
88(7)
6.4 Robust Performance
95(3)
6.5 Human Motor Control
98(3)
6.5.1 Computational Models of Upper Limb Motion
98(1)
6.5.2 Unimpaired Motion Data Collection
98(2)
6.5.3 Data Analysis
100(1)
6.6 Computational Model Development
101(1)
6.7 Results
102(5)
6.8 Conclusions
107(4)
References
108(3)
7 Clinical Application: Goal-Orientated Stroke Rehabilitation
111(10)
7.1 System Description and Set-Up
111(4)
7.1.1 Outcome Measures
114(1)
7.2 Results
115(3)
7.2.1 Assisted Tracking Performance
115(1)
7.2.2 Unassisted Tracking Performance
116(1)
7.2.3 Clinical Outcome Measures
117(1)
7.3 Discussion
118(1)
7.4 Conclusions
118(3)
References
119(2)
8 Electrode Array Control Design
121(20)
8.1 Modeling of a Single Array
121(1)
8.2 General Array Control Framework
122(4)
8.3 Subspace Identification
126(8)
8.3.1 Selection Using Experimental Data
126(4)
8.3.2 Selection Using Structural Knowledge
130(2)
8.3.3 General Stimulation Subspace Identification Procedure
132(2)
8.4 Model Identification
134(1)
8.5 Case Study: Functional Hand and Wrist Motion
135(5)
8.5.1 Unrestricted Stimulation Space
136(3)
8.5.2 Stimulation Subspace
139(1)
8.6 Conclusions
140(1)
References
140(1)
9 Clinical Application: Fully Functional Stroke Rehabilitation
141(22)
9.1 General Integrated Control Framework
142(8)
9.2 System Description and Set-Up
150(6)
9.2.1 Task Design
151(1)
9.2.2 System Software
151(2)
9.2.3 Motion Tracking
153(1)
9.2.4 Stimulation Hardware
154(1)
9.2.5 Control Design
155(1)
9.3 Results
156(5)
9.3.1 Unimpaired Participants
157(2)
9.3.2 Stroke Participants
159(2)
9.4 Discussion
161(1)
9.5 Conclusions
162(1)
References
162(1)
10 Conclusions and Future Research Directions
163(12)
10.1 Elimination of Identification and Manual Controller Tuning
164(8)
10.2 Wearable ES Technology
172(2)
10.3 Wider Application Domains and Greater Scope
174(1)
References 175
Over the last ten years Dr. Freeman has developed new healthcare technologies combining robotics and electrical stimulation to enable people with upper limb impairments to perform functional tasks. Over this time he has worked closely with clinicians (including former IFESS president Prof Jane Burridge), patients and carers. These include five clinical trials using technology he has developed, as well as numerous smaller studies and user-led design sessions. His focus has been to understand and define clinical problems within an engineering perspective and translate this into usable solutions. Dr. Freeman's background in adaptive and learning control of robotic structures has enabled him to rigorously tackle the challenge of designing systems that provide high performance in the face of significant model uncertainty/variability, restrictive clinical conditions, and complex dynamics/tasks. These problems have led to a productive interaction in his research between theory and practice (the latter generally providing new problems that need an algorithmic or theoretical solution). This is reflected in my publications which include 70 peer-reviewed journal papers and 130 peer-reviewed conference papers covering the spectrum from control application, control theory, rehabilitation engineering, biomechanics, clinical studies and user perspectives. The research he has led in control of ES has elicited two best conference paper awards (ICORR '09, UKACC '12), and two best journal paper awards (most recently `2013 IEEE Control Systems Society Outstanding Paper Award' based on `impact on the field of systems and control'). For example, his work using iterative learning control and adaptive control for stroke rehabilitation has met with an enthusiastic response from the research community, with invited workshops at IEEE BioRob Conference 2012, 18th IFESS Conference 2013, World Congress in NeuroRehabilitation 2014, as well as a plenary at IEEE International Workshop on nD Systems 2013. Citations for my research using both ES and advanced control in the last 5 years exceed 500. In a wider content, the last 10 years has seen a steady increase in research papers, funding calls, and postgraduate courses related to assistive technologies. An example of the latter is an EU-funded MSc in Advanced Rehabilitation Technologies involving 10 EU partners that he is currently helping to develop. His work in control design for systems combining ES and mechanical support is motivated by the lack of model-based controllers that reach clinical or commercial application and it has elicits an international response, from TU Berlin, ETH Zurich, U. Washington and U. California who have since applied iterative learning control-based FES to the lower limb.