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E-raamat: Applied Biomechatronics Using Mathematical Models

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(Research Consulting Services (garzaulloa.org) / University of Texas, El Paso, TX, USA)
  • Formaat: PDF+DRM
  • Ilmumisaeg: 16-Jun-2018
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128125953
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Applied Biomechatronics Using Mathematical ModelsSuurem pilt
  • Formaat: PDF+DRM
  • Ilmumisaeg: 16-Jun-2018
  • Kirjastus: Academic Press Inc
  • Keel: eng
  • ISBN-13: 9780128125953
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Applied Biomechatronics Using Mathematical Models provides an appropriate methodology to detect and measure diseases and injuries relating to human kinematics and kinetics. It features mathematical models that, when applied to engineering principles and techniques in the medical field, can be used in assistive devices that work with bodily signals. The use of data in the kinematics and kinetics analysis of the human body, including musculoskeletal kinetics and joints and their relationship to the central nervous system (CNS) is covered, helping users understand how the complex network of symbiotic systems in the skeletal and muscular system work together to allow movement controlled by the CNS.

With the use of appropriate electronic sensors at specific areas connected to bio-instruments, we can obtain enough information to create a mathematical model for assistive devices by analyzing the kinematics and kinetics of the human body. The mathematical models developed in this book can provide more effective devices for use in aiding and improving the function of the body in relation to a variety of injuries and diseases.

  • Focuses on the mathematical modeling of human kinematics and kinetics
  • Teaches users how to obtain faster results with these mathematical models
  • Includes a companion website with additional content that presents MATLAB examples
About the Author xvii
Preface xix
Chapter 1 Introduction to Biomechatronics/Biomedical Engineering
1(52)
1.0 Introduction
1(1)
1.1 The Evolution From Basic Disciplines of Engineering to Multidisciplinary Engineering Branches
2(5)
1.2 Biomechatronics Implemented Using Mathematical Models
7(8)
1.3 Neurological Diseases
15(3)
1.4 Biomechatronics Solutions for Neurological Diseases
18(21)
1.5 The Near Future of Biomechatronics Devices for Neurological Diseases
39(3)
1.6 Suggested Research for
Chapter 1
42(11)
1.6.1 Answers for Suggested Research
Chapter 1
43(5)
References
48(5)
Chapter 2 Introduction to Human Neuromusculoskeletal Systems
53(66)
2.0 Introduction
54(1)
2.1 Introduction to Human Body System
55(6)
2.1.1 Organ and Organs System
56(3)
2.1.2 Organism
59(2)
2.2 Introduction to the Musculoskeletal System
61(22)
2.2.1 Skeletal Subsystem
64(7)
2.2.2 Muscular Subsystem
71(8)
2.2.3 Joints Subsystem
79(4)
2.3 Introduction to the Nervous System
83(22)
2.3.1 Human Brain
88(6)
2.3.2 Spinal Cord
94(3)
2.3.3 Nerves
97(8)
2.4 Suggested Research for
Chapter 2
105(3)
2.4.1 From Section 2.1 Introduction to Human Body System
105(1)
2.4.2 From Section 2.2 Introduction to the Musculoskeletal System
106(2)
2.4.3 From Section 2.3 Introduction to the Nervous System
108(1)
2.5 Answers to Suggested Research for
Chapter 2
108(11)
2.5.1 Answers Questions Section 2.1 Introduction to Human Body System
108(1)
2.5.2 Answers Questions Section 2.2 Introduction to the Musculoskeletal System
109(4)
2.5.3 Answers Questions Section 2.3 Introduction to the Nervous System
113(3)
References
116(3)
Chapter 3 Kinematic and Kinetic Measurements of Human Body
119(60)
3.0 Introduction
119(1)
3.1 Data Acquisition
120(38)
3.1.1 Electromyography
124(18)
3.1.2 Ground Reaction Force
142(16)
3.2 Introduction to Alternate Sensors for Gait and Motion Analysis
158(7)
3.2.1 Image Processing Using Video Cameras
158(4)
3.2.2 Wearable Sensors
162(3)
3.3 Conclusions
165(1)
3.4 Summary of Equations of This
Chapter 3
165(1)
3.5 Suggested Research for
Chapter 3
166(2)
3.6 Answers to Suggested Research
168(11)
References
174(5)
Chapter 4 Experiment Design, data Acquisition and Signal Processing
179(60)
4.0 Introduction
180(1)
4.1 GV From Kinetics Measurement of 3D-GRF
181(9)
4.1.1 Example: GV From Kinetics Measurement of GRF
182(8)
4.2 Joint Angles as Kinematic Measurement
190(8)
4.2.1 Angular Kinematics
191(7)
4.3 Synchronized Signals for Kinetics and Kinematics Measurements
198(6)
4.3.1 Example Signals Synchronized From Kinetics and Kinematics Measurements
199(5)
4.4 Signals Synchronized for: Kinetics, Kinematic, and Physiological Responses Measurements
204(9)
4.4.1 Example of Signals Synchronized for: Kinetics, Kinematics, and Physiological Responses Measurements
205(8)
4.5 Experiment Design (DOE)
213(13)
4.5.1 General Approach for Experimental Design for Kinematics, Kinetics, and Physiological Responses Measurement of the Human Body
214(5)
4.5.2 Examples of DOEs in Biomechanics
219(7)
4.6 Conclusion
226(1)
4.7 Summary of Equations
226(2)
4.8 Research for
Chapter 4
228(11)
4.8.1 Suggested Research
228(1)
4.8.2 Answers to Suggested Research
228(6)
References
234(5)
Chapter 5 Methods to Develop Mathematical Models: Traditional Statistical Analysis
239(134)
5.0 Introduction
240(1)
5.1 Statistical Analysis: Traditional Method
241(97)
5.1.1 Introduction to Statistics
241(3)
5.1.2 Basic Variables in Statistics
244(8)
5.1.3 Probability Models
252(2)
5.1.4 Probability Distributions
254(17)
5.1.5 Statistical Inference Using Statistical Hypotheses Testing
271(47)
5.1.6 Time Series and Autocorrelation in Autoregressive Mathematical Models
318(11)
5.1.7 Special Application of Mathematical Models for Analysis of Continuous Glucose Monitor for Diabetic subjects
329(9)
5.2 Conclusions
338(1)
5.3 Summary of Equations
339(13)
5.4 Research for
Chapter 5
352(21)
5.4.1 Suggested Research
352(7)
5.4.2 Answers to Suggested Research
359(10)
References
369(4)
Chapter 6 Application of Mathematical Models in Biomechatronics: Artificial Intelligence and Time-Frequency Analysis
373(152)
6.0
Chapter Introduction
374(2)
6.1 Time-Frequency Domain
376(3)
6.2 Fourier Transform
379(4)
6.2.1 Example: Fourier Transform
380(3)
6.3 Median and Mean Frequency Analysis in sEMG
383(8)
6.3.1 Applied Research: Muscle Fatigue Detected Using sEMG Mean Frequency Analysis
384(6)
6.3.2 Research Papers on Muscle Fatigue
390(1)
6.4 Wavelet Transform
391(15)
6.4.1 Wavelet One-Stage Filtering Example
395(3)
6.4.2 Wavelet and Wavelet Transforms
398(3)
6.4.3 Example: Wavelet Multiple-Level Filtering
401(4)
6.4.4 Research Papers on Wavelet Transform
405(1)
6.5 Artificial Intelligence
406(64)
6.5.1 Machine Learning
406(2)
6.5.2 Unsupervised Machine Learning
408(1)
6.5.3 Unsupervised Learning, Partitional Clustering: K-Means Algorithm
409(8)
6.5.4 Unsupervised Learning, Hierarchical Clustering
417(7)
6.5.5 Supervised Machine Learning
424(2)
6.5.6 Supervised Learning, Classification Tree Analysis (CTA)
426(9)
6.5.7 Applied Research: Supervised Learning, Regression Tree Analysis (RTA)
435(16)
6.5.8 Artificial Neural Network, Mathematical Models
451(1)
6.5.9 Artificial Neural Network, Supervised Learning
452(6)
6.5.10 Applied Research: Artificial Neural Network, Supervised Learning
458(10)
6.5.11 Summary of Machine Learning
468(2)
6.6 Fuzzy Inference Systems, Mathematical Models
470(21)
6.6.1 Soft Computing
470(6)
6.6.2 Example FIS of the Inverted Pendulum Using MATLAB? Fuzzy Toolbox
476(4)
6.6.3 Normalized Synchronized Dynamic Signals Fuzzy Sets Evaluated by Human Gait Phases
480(2)
6.6.4 Applied Research 1 of 2 Parts: Normalized Synchronized Dynamic Signals Fuzzy Sets Evaluated in the Human Gait Phases
482(9)
6.7 Fuzzy Relations and Fuzzy Similarities
491(5)
6.8 Applied Research 2 of 2 Parts: Comparative Analysis of Normalized Synchronized Dynamic Signals: Fuzzy Sets Evaluated in the Human Gait Phases Between Control Group and Subject Under Study for Human Dynamic Conclusions
496(17)
6.9 Research Paper on Fuzzy Logic
513(2)
6.10 Conclusion for
Chapter 6
515(1)
6.11 Summary of Equations
515(6)
6.12 Research for
Chapter 6
521(4)
References
521(4)
Chapter 7 Case Studies of Applied Biomechatronics Solutions Based on Mathematical Models
525(102)
7.0 Introduction
527(1)
7.1 The Eight Basic Pathologic Gaits Attributed to Neurological Conditions
527(17)
7.1.1 Hemiplegic Gait as Shown in Fig. 7.1 (1). It Is Most Commonly Seen in the Case of Stroke
527(3)
7.1.2 Diplegic Gait as Shown in Fig. 7.1 (2). It Is Seen in Bilateral Periventricular Lesions, Such as Those Seen in the Case of Cerebral Palsy
530(3)
7.1.3 Neuropathic Gait as Shown in Fig. 7.1 (3). It Is Also Known as Steppage Gait and Equine Gait
533(2)
7.1.4 Myopathic Gait as Shown in Fig. 7.1(4). Also Called Waddling Gait, is a Form of Gait Abnormality
535(1)
7.1.5 Choreiform Gait as Shown in Fig. 7.1 (5). It is also Known as Hyperkinetic Gait
536(2)
7.1.6 Ataxic Gait as Shown in Fig. 7.1(6). Is Mostly Seen in the Case of Cerebellar Diseases
538(2)
7.1.7 Parkinsonian Gait as Shown in Fig. 7.1(7). In This Gait, the Patient Will Have Rigidity and Bradykinesia
540(2)
7.1.8 Sensory Gait as Shown in Fig. 7.1 (8). Sensory Ataxic Gait Occurs When There Is a Loss of the Proprioceptive Input as Feet Touch the Ground
542(2)
7.2 Detection of Parameters in the Eight Basic Pathologic Gaits Attributed to Neurological Conditions
544(5)
7.2.1 Important Muscles to Detect the Eight Basic Pathologic Gaits Attributed to Neurological Conditions
547(2)
7.2.2 Important Joint Angles to Detect the Eight Basic Pathologic Gaits Attributed to Neurological Conditions
549(1)
7.3 Experimental Design for Detection and Identification of the Eight Basic Pathologic Gaits Attributed to Neurological Conditions
549(3)
7.4 Fuzzy Mathematical Models for the Analysis of the Eight Basic Pathologic Gaits Attributed to Neurological Conditions
552(1)
7.5 Fuzzy Mathematical Models' Definitions of T-Fuzzy Inference and Fuzzy Similarities for the Human Dynamic Analysis
552(6)
7.6 Challenge Research Biomechatronics Devices for the Eight Basic Pathologic Gaits Attributed to Neurological Conditions
558(47)
7.6.1 Hemiplegic Gait and Stroke
564(4)
7.6.2 Diplegic Gait and Cerebral Palsy
568(4)
7.6.3 Neuropathic Gait and Peripheral Nerve Disease Foot Drop
572(5)
7.6.4 Myopathic Gait and Muscular Dystrophy
577(5)
7.6.5 Choreiform Gait and Basal Ganglia Disorders
582(4)
7.6.6 Ataxic Gait and Cerebellum Irregular Body Coordination
586(4)
7.6.7 Parkinsonian Gait (Parkinson) and Substantia Nigra of the Midbrain Disorders
590(5)
7.6.8 Sensory Gait and Diabetes
595(4)
7.6.9 Other Neurologic Diseases
599(6)
7.7 Top Challenge Researches
605(8)
7.7.1 Top Challenge Research #1: Implementation of a System for the Detection, Measurement, and Identification of the Eight Basic Pathologic Gaits Attributed to Neurological Conditions
605(1)
7.7.2 Top Challenge Research #2: Classification of Dynamic Human Movements During Gait for Prediction of Falls in the Elderly (≤65 years)
606(1)
7.7.3 Top Challenge Research #3: Ergonomics Measures BASED on Interaction of Machines and the Human Dynamic Analysis to Avoid Fatigue and Injuries in the Daily Job
606(7)
7.8 Future Projects and Reminder to Register Your Project
613(3)
7.8.1 Future Projects for Applied Biomechatronics Using Mathematical Models
614(1)
7.8.2 Register Your Project
615(1)
7.9 Summary of Equations
616(11)
References
618(9)
Index 627
Dr. Jorge Garza-Ulloa is the CEO/Director of Garzaulloa.org Research Consulting Services. He graduated from the doctoral program in Electrical & Computer Eng. at University of Texas El Paso (UTEP). Dr. Garza-Ulloa has focused his research in the development of mathematical models for Electrical and Computer Engineering: Biomedical applications, such as his Mathematical Model for the Validation of the Ground Reaction Force Sensor in Human Gait Analysis, the Mathematical model to predict Transition-to-Fatigue during isometric exercise on muscles of the lower extremities, his mathematical model to be used in the Assessment and evaluation of dynamic behavior of muscles with special reference to subjects with Diabetes Mellitus (dissertation) . Besides: his natural fuzzy logic model for differential analysis in muscle/joint activation point using infer T equation for muscle/joint, Extension of the T-Fuzzy inference equation using Fuzzy Mapping for Human Gait Analysis to identify the eight-basic pathologic gait attributed to neurological conditions (Academic press book: Applied Biomechatronic Using Mathematical Models), many others. In this book introduces: Cognitive Learning models and its relationship with neuroscience of reasoning under Cognitive Learning- Reasoning (CL&R) applying Cognitive Computing (CC), and many others to be integrated at the Proposed General Architecture framework of a Cognitive Computing Agents System (AI-CCAS). Dr. Garza-Ulloa has been the recipient of numerous honors and awards including a University of Texas at El Paso Graduate School Research Award, Research Schellenberg Foundation, Stern Foundation, Elsevier grants, and others. Dr. Garza-Ulloa is currently teaching at the University of Texas at El Paso USA and continue his Biomedical Engineering research at Research Consultant Services at El Paso Texas, USA.
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