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Author Fang, Bin, author.

Title Wearable technology for robotic manipulation and learning / Bin Fang, Fuchun Sun, Huaping Liu, Chunfang Liu, Di Guo
Published Singapore : Springer, [2020]
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Description 1 online resource (xxiv, 208 pages) : illustrations
Contents Intro -- Foreword -- Preface -- Acknowledgements -- Contents -- About the Authors -- Acronyms -- Mathematical Notation -- Part I Background -- 1 Introduction -- 1.1 The Overview of Wearable Devices -- 1.1.1 Wrist-Worn -- 1.1.2 Head Mounted -- 1.1.3 Body Equipped -- 1.1.4 Smart Garment -- 1.1.5 Smart Shoes -- 1.1.6 Data Gloves -- 1.2 Sensors of Wearable Device -- 1.2.1 Motion Capture Sensor -- 1.2.2 Tactile Sensors -- 1.2.3 Physiological Parameter Measurement Sensors -- 1.3 Wearable Computing Algorithms -- 1.3.1 Motion Capture Related Algorithms -- 1.3.2 Motion Recognition Related Algorithms
1.3.3 Comparison of Different Wearable Computing Algorithms -- 1.4 Applications -- 1.4.1 Interaction -- 1.4.2 Healthcare -- 1.4.3 Manipulation Learning -- 1.5 Summary -- References -- Part II Wearable Technology -- 2 Wearable Sensors -- 2.1 Inertial Sensor -- 2.1.1 Analysis of Measurement Noise -- 2.1.2 Calibration Method -- 2.1.3 Experimental Results -- 2.2 Tactile Sensor -- 2.2.1 Piezo-Resistive Tactile Sensor Array -- 2.2.2 Capacitive Sensor Array -- 2.2.3 Calibration and Results -- 2.3 Summary -- References -- 3 Wearable Design and Computing -- 3.1 Introduction -- 3.2 Design
3.2.1 Inertial and Magnetic Measurement Unit Design -- 3.2.2 Wearable Design -- 3.3 Motion Capture Algorithm -- 3.3.1 Models of Inertial and Magnetic Sensors -- 3.3.2 QEKF Algorithm -- 3.3.3 Two-Step Optimal Filter -- 3.4 Experimental Results -- 3.4.1 Orientations Assessment -- 3.4.2 Motion Capture Experiments -- 3.5 Summary -- References -- 4 Applications of Developed Wearable Devices -- 4.1 Gesture Recognition -- 4.1.1 ELM-Based Gestures Recognition -- 4.1.2 CNN-Based Sign Language Recognition -- 4.1.3 Experimental Results -- 4.2 Tactile Interaction -- 4.3 Tactile Perception
4.3.1 Tactile Glove Description -- 4.3.2 Visual Modality Representation -- 4.3.3 Tactile Modality Representation -- 4.3.4 Visual-Tactile Fusion Classification -- 4.3.5 Experimental Results -- 4.4 Summary -- References -- Part III Manipulation Learning from Demonstration -- 5 Learning from Wearable-Based Teleoperation Demonstration -- 5.1 Introduction -- 5.2 Teleoperation Demonstration -- 5.2.1 Teleoperation Algorithm -- 5.2.2 Demonstration -- 5.3 Imitation Learning -- 5.3.1 Dynamic Movement Primitives -- 5.3.2 Imitation Learning Algorithm -- 5.4 Experimental Results
5.4.1 Robotic Teleoperation Demonstration -- 5.4.2 Imitation Learning Experiments -- 5.4.3 Skill-Primitive Library -- 5.5 Summary -- References -- 6 Learning from Visual-Based Teleoperation Demonstration -- 6.1 Introduction -- 6.2 Manipulation Learning of Robotic Hand -- 6.3 Manipulation Learning of Robotic Arm -- 6.4 Experimental Results -- 6.4.1 Experimental Results of Robotic Hand -- 6.4.2 Experimental Results of Robotic Arm -- 6.5 Summary -- References -- 7 Learning from Wearable-Based Indirect Demonstration -- 7.1 Introduction -- 7.2 Indirect Wearable Demonstration -- 7.3 Learning Algorithm
Summary Over the next few decades, millions of people, with varying backgrounds and levels of technical expertise, will have to effectively interact with robotic technologies on a daily basis. This means it will have to be possible to modify robot behavior without explicitly writing code, but instead via a small number of wearable devices or visual demonstrations. At the same time, robots will need to infer and predict humans' intentions and internal objectives on the basis of past interactions in order to provide assistance before it is explicitly requested; this is the basis of imitation learning for robotics. This book introduces readers to robotic imitation learning based on human demonstration with wearable devices. It presents an advanced calibration method for wearable sensors and fusion approaches under the Kalman filter framework, as well as a novel wearable device for capturing gestures and other motions. Furthermore it describes the wearable-device-based and vision-based imitation learning method for robotic manipulation, making it a valuable reference guide for graduate students with a basic knowledge of machine learning, and for researchers interested in wearable computing and robotic learning
Bibliography Includes bibliographical references
Notes Description based on online resource; title from digital title page (viewed on December 11, 2020)
Subject Automation.
Robotics.
User interfaces (Computer systems)
Automation.
Robotics.
User interfaces (Computer systems)
Form Electronic book
Author Guo, Di, author.
Liu, Chunfang, author.
Liu, Huaping, author.
Sun, Fuchun, author.
ISBN 9789811551246
9811551243