Advances in Motor Neuroprostheses

✍ Scribed by Ramana Vinjamuri (editor)

• Publisher: Springer
• Year: 2020
• Tongue: English
• Leaves: 169
• Edition: 1st ed. 2020
• Category: Library

✦ Synopsis

This book provides a comprehensive review of recent developments in the field of motor neuroprosthetics and brain-machine interfaces. Chapters in this book are provided by leading experts in the field and include topics such as the design and control of multidimensional prosthetics and exoskeletons, deep brain stimulation, functional electrical stimulation, deep learning for brain machine interfaces, biofeedback, and cognitive intent for adaptation of motor prostheses. This book is a great resource for undergraduate and graduate students, researchers, engineers from related disciplines, entrepreneurs, and anyone interested in the latest progress in the field of motor neuroprostheses.

✦ Table of Contents

Preface
Contents
Contributors
Application of Reinforcement and Deep Learning Techniques in Brain–Machine Interfaces
1 Introduction
2 Deep Learning in BMI Studies
3 Reinforcement Learning in BMI Studies
4 Application of Deep Learning in Human–Robot Interaction: A Case Study
4.1 EEG Signal Analysis
4.2 Convolution Neural Network Architecture
5 Conclusion
References
Subject-Specific Muscle Activation Patterns in Athletic and Orthopedic Populations: Considerations for Using Surface Electromyography in Assistive and Biofeedback Device Applications
1 Muscle Activation Measurement and Analysis
2 Dancers Using Subject-Specific Muscle Activation Patterns During Turns
2.1 Double vs. Single Piqué Turn
2.2 Triple vs. Double Piqué Turns
2.3 Take-Homes from This Exemplar Comparison
3 Baseball Pitchers Using Subject-Specific Hamstring Muscle Recruitment
4 Preoperative Shoulder Arthroplasty Patients Using Subject-Specific Movement Mechanics During Arm Elevation Tasks
5 Concluding Remarks
References
Kineto-Dynamic Modeling of Human Upper Limb for Robotic Manipulators and Assistive Applications
1 Introduction
2 Experimental Setup for Data Acquisition
3 Modeling
3.1 Kinematic Model of Human Upper Limb
3.1.1 Markers Placement
3.2 Kinematic Model of the Human Hand
4 Motion Identification
4.1 Model Calibration
4.2 Motion Identification
5 Principal Functions for Upper Limb Movement Generation
5.1 Experiments
5.2 Data Analysis
5.2.1 A Functional Extension of PCA
5.3 Results
6 Postural Hand Synergies During Environmental Constraint Exploitation
6.1 Pre-processing
6.1.1 Pre-shaping Analysis
6.1.2 Contact Analysis
6.1.3 Differences Between Pre and During Contact
6.2 Results
6.2.1 Pre-shaping Analysis
6.2.2 Contact Analysis
6.2.3 Differences Between Pre and During Contact
6.2.4 Inference and Statistical Relevance
7 Implications for Robotics and Motor Control
Appendix
References
Learning from the Human Hand: Force Control and Perception Using a Soft-Synergy Prosthetic Hand and NoninvasiveHaptic Feedback
1 Introduction
2 Materials and Method
2.1 Subjects
2.2 Experiment Apparatus
2.2.1 SoftHand-Pro (SHP)
2.2.2 Clenching Upper-Limb Force Feedback Device (CUFF)
2.2.3 Gravity Compensation
2.2.4 Data Recording
2.3 Experimental Designs
2.3.1 Study 1
2.3.2 Study 2
3 Study 1 Results: Fine Control of Grasping Force During Hand-Object Interactions
4 Study 2 Results: Inter-Limb Transfer of Perceptual Information About Grasping Force
5 Discussion
5.1 Context-Dependent Hybrid Gain Myoelectric Controller
5.2 Inter-Limb Transfer of Perceptual Information in Closed-Loop Prosthetic Systems
5.3 Open Questions and Future Research
References
Design of a Soft Glove-Based Robotic Hand Exoskeleton with Embedded Synergies
1 Introduction
2 Methods
2.1 Actuator Assembly
2.2 Hand Component
2.3 Electrical Design
3 Discussion
4 Conclusion
References
Model Predictive Control-Based Knee Actuator Allocation During a Standing-Up Motion with a Powered Exoskeleton and Functional Electrical Stimulation
1 Introduction
2 System Dynamics
3 Standing Motion Planning
4 Feedback Control Development
5 Model Predictive Control-Based Ratio Allocation Method
5.1 Muscle Force Generation and Fatigue Model
5.2 Optimization Problem
6 Results
6.1 Simulation
7 Conclusion
References
Deep Brain Stimulation for Gait and Postural Disturbances in Parkinson's Disease
1 Introduction
1.1 Parkinson's Disease
1.2 Gait and Postural Disturbances in PD
1.3 Treatment Options for Gait and Postural Disturbances in PD
1.4 Targeting of DBS
2 Methods
3 STN/GPi DBS
3.1 Neuroanatomy of STN/GPi
3.2 Proposed Mechanisms of Action
3.3 Effects of Stimulation Location and Frequency
3.4 Targeting of STN/GPi DBS
3.5 Limitations
4 PPN DBS
4.1 Neuroanatomy of PPN
4.2 Proposed Mechanisms of Action
4.3 Effects of Stimulation Location and Frequency
4.4 Targeting of PPN DBS
4.5 Limitations
5 SNr DBS
5.1 Neuroanatomy of SNr
5.2 Proposed Mechanisms of Action
5.3 Effects of Stimulation Location and Frequency
5.4 Targeting of SNr DBS
5.5 Limitations
6 Discussion
References
Cognitive and Physiological Intent for the Adaptation of Motor Prostheses
1 Introduction
2 Research Approach 1: Identifying Operational Conditions of Motor Prosthetic Devices That Enhance User Sense of Agency
2.1 Neuromuscular Disability Can Lead to a Sense of Disengagement From One's Own Body
2.2 Sense of Agency and Its Consideration for Motor Prostheses to Rehabilitate Function
2.3 Linking Agency to Greater Movement Performance
2.4 Physiological Patterns as Implicit Measures for Agency
2.5 Utilizing Reward-Based Rehabilitation to Enhance Agency and Performance
2.6 Sensory Feedback to Induce Greater Agency
2.7 Dependence of User Agency to Device Sensitivity
3 Research Approach 2: Utilizing Sensory Feedback to Train Consistent Movement Responses for Better Rehabilitation and Improved Use of Motor Prostheses
3.1 Rationale to Training the User for Better Integration to a Myoelectric Motor Prosthesis
3.2 Sensory Feedback for Movement Training
3.3 Strategic Features in Sensory Feedback Training of Movement
3.3.1 Sensory Feedback Training for Real-Time Performance Versus Retention
3.3.2 Feedback Complexity in Regulating Movement Performance
3.3.3 Sensory Feedback Integration for Myoelectric Prosthetic Control
3.4 Machine Learning for Movement Control and Intent Detection
3.5 Multisensory Platform to Train Users for Cognitive Integration to Motor Prostheses Under Myoelectric Control
4 Conclusions
References
Index