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E-raamat: Computer Vision for Assistive Healthcare

Edited by (Researcher, National Research Council of Italy), Edited by (Researcher, Department of Mathematics and Computer Science, University of Catania, Italy)
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Computer Vision for Assistive Healthcare describes how advanced computer vision techniques provide tools to support common human needs, such as mental functioning, personal mobility, sensory functions, daily living activities, image processing, pattern recognition, machine learning and how language processing and computer graphics cooperate with robotics to provide such tools. Users will learn about the emerging computer vision techniques for supporting mental functioning, algorithms for analyzing human behavior, and how smart interfaces and virtual reality tools lead to the development of advanced rehabilitation systems able to perform human action and activity recognition.

In addition, the book covers the technology behind intelligent wheelchairs, how computer vision technologies have the potential to assist blind people, and about the computer vision-based solutions recently employed for safety and health monitoring.

  • Gives the state-of-the-art computer vision techniques and tools for assistive healthcare
  • Includes a broad range of topic areas, ranging from image processing, pattern recognition, machine learning to robotics, natural language processing and computer graphics
  • Presents a wide range of application areas, ranging from mobility, sensory substitution, and safety and security, to mental and physical rehabilitation and training
  • Written by leading researchers in this growing field of research
  • Describes the outstanding research challenges that still need to be tackled, giving researchers good indicators of research opportunities
Contributors xi
About the Editors xvii
Preface xxi
1 Computer Vision for Sight
1(50)
Feng Hu
Hao Tang
Aleksandr Tsema
Zhigang Zhu
1.1 Introduction
2(5)
1.1.1 Problem Statement
3(1)
1.1.2 Important Considerations
3(4)
1.2 A Recommended Paradigm
7(5)
1.2.1 Environmental Modeling
8(2)
1.2.2 Localization Algorithms
10(1)
1.2.3 Assistive User Interfaces
11(1)
1.3 Related Work
12(6)
1.3.1 Omnidirectional-Vision-Based Indoor Localization
13(4)
1.3.2 Other Vision-Based Indoor Localization
17(1)
1.3.3 Assistive Technology and User Interfaces
17(1)
1.4 An Omnidirectional Vision Approach
18(26)
1.4.1 User Interfaces and System Consideration
20(2)
1.4.2 Path Planning for Scene Modeling
22(7)
1.4.3 Machine Learning for Place Recognition
29(5)
1.4.4 Initial Localization Using Image Retrieval
34(4)
1.4.5 Localization Refinement With 3D Estimation
38(6)
1.5 Conclusions and Discussions
44(7)
Glossary
45(1)
Acknowledgments
45(1)
References
46(5)
2 Computer Vision for Cognition
51(24)
Corneliu Florea
Laura Florea
Constantin Vertan
2.1 Why Eyes Are Important for Human Communication
52(4)
2.1.1 Eyes in Nonverbal Communication
53(1)
2.1.2 Eye Movements
54(2)
2.2 Gaze Direction Recognition and Tracking
56(3)
2.2.1 Eye Tracking Metrics
58(1)
2.3 Eye Tracking and Cognitive Impairments
59(1)
2.4 Computer Vision Support for Diagnosis of Autism Spectrum Disorders
59(5)
2.4.1 Methods and Solutions
61(2)
2.4.2 Results
63(1)
2.5 Computer Vision Support for the Identification of Dyslexia
64(2)
2.6 Computer Vision Support for Identification of Anxiety Disorders
66(2)
2.6.1 Assessing Phobias
66(1)
2.6.2 Studying PTSD
67(1)
2.7 Computer Vision Support for Identification of Depression and Dementia
68(1)
2.8 Conclusions and Discussion
68(7)
Acknowledgments
69(1)
References
70(5)
3 Real-Time 3D Tracker in Robot-Based Neurorehabilitation
75(30)
Fabio Stroppa
Mine Sarac Stroppa
Simone Marcheschi
Claudio Loconsole
Edoardo Sotgiu
Massimiliano Solazzi
Domenico Buongiorno
Antonio Frisoli
3.1 Introduction
76(2)
3.2 Tracking Module
78(16)
3.2.1 Two-Dimensional Preprocessing
80(1)
3.2.2 Three-Dimensional Processing
81(8)
3.2.3 Assessment
89(5)
3.3 Robotic Devices
94(4)
3.3.1 Arm Light Exoskeleton
94(1)
3.3.2 Wrist Exoskeleton
95(2)
3.3.3 Hand Orthosis
97(1)
3.4 Overall System Experiments
98(3)
3.5 Discussion and Conclusion
101(4)
References
102(3)
4 Computer Vision and Machine Learning for Surgical Instrument Tracking
105(22)
Nicola Rieke
Federico Tombari
Nassir Navab
4.1 Introduction
106(3)
4.1.1 Potential Benefit of Surgical Instrument Tracking in Retinal Microsurgery
107(1)
4.1.2 Challenges of Computer Vision in Medical Applications
108(1)
4.2 Overview of the State of the Art
109(1)
4.3 Method
110(8)
4.3.1 Random Forests
111(1)
4.3.2 Template Definition
112(1)
4.3.3 Tracking
112(1)
4.3.4 Two-Dimensional Pose Estimation
113(2)
4.3.5 Feed-Forward Pipeline
115(1)
4.3.6 Robust Pipeline via Online Adaptation and Closed Loop
116(2)
4.4 Performance Evaluation
118(2)
4.4.1 Comparison to the State of the Art
119(1)
4.42 Comparison of the Suggested Pipelines
120(1)
44.3 Component Analysis for Robustness
121(1)
4.5 Conclusion and Future Work
122(5)
Acknowledgments
123(1)
References
123(4)
5 Computer Vision for Human-Machine Interaction
127(20)
Qiuhong Ke
Jun Liu
Mohammed Bennamoun
Senjian An
Ferdous Sohel
Farid Boussaid
5.1 Background of Human-Machine Interaction
128(1)
5.1.1 Human-Machine Interfaces
128(1)
5.1.2 Gesture-Based Human-Machine Interaction
129(1)
5.2 Data Acquisition for Gesture Recognition
129(1)
5.3 Computer Vision-Based Gesture Recognition
130(12)
5.3.1 Convolutional Neural Networks
131(1)
5.3.2 RGB-Based Gesture Recognition
132(6)
5.3.3 Depth-Based Gesture Recognition
138(2)
5.3.4 Skeleton-Based Gesture Recognition
140(2)
5.4 Conclusion
142(5)
Acknowledgments
143(1)
References
143(4)
6 Computer Vision for Ambient Assisted Living
147(36)
Sara Colantonio
Giuseppe Coppini
Daniela Giorgi
Maria-Aurora Morales
Maria A. Pascali
6.1 Introduction
148(2)
6.1.1
Chapter Scope
149(1)
6.2 Computer Vision for AAL
150(1)
6.3 Monitoring in Personalized Healthcare and Wellness: The State of the Art
151(10)
6.3.1 Vital Signs
152(2)
6.3.2 Posture and Movement
154(3)
6.3.3 Anthropometric Parameters
157(2)
6.3.4 Emotions, Expressions, and Individual Wellness
159(2)
6.4 Methodological, Clinical, and Societal Challenges
161(2)
6.5 A Possible Solution: The Wize Mirror
163(10)
6.5.1 Self-Measurement
164(6)
6.5.2 Education and Coaching
170(2)
6.5.3 User Experience
172(1)
6.5.4 Wize Mirror Validation
172(1)
6.6 Conclusion
173(10)
Acknowledgments
174(1)
References
174(9)
7 Computer Vision for Egocentric (First-Person) Vision
183(28)
Mariella Dimiccoli
7.1 Introduction
184(1)
7.2 Contextual Understanding
185(6)
7.3 First-Person Activity Recognition
191(7)
7.3.1 Ambulatory Activities
191(1)
7.3.2 Person-to-Object Interactions
192(3)
7.3.3 Person-to-Person Interactions
195(2)
7.3.4 Ego-Engagement in Browsing Scenarios
197(1)
7.4 First-Person Activity Forecasting
198(3)
7.5 First-Person Social Interaction Analysis
201(3)
7.6 Discussion and Conclusions
204(7)
References
207(4)
8 Computer Vision for Augmentative and Alternative Communication
211(38)
Sethuraman Panchanathan
Meredith Moore
Hemanth Venkateswara
Shayok Chakraborty
Troy McDaniel
8.1 Introduction and Background
213(6)
8.1.1 The Communication Process
213(1)
8.1.2 Diversity of Communication
214(1)
8.1.3 Complex Communication Needs
214(1)
8.1.4 Introduction to Augmentative Alternative Communication
215(4)
8.2 Computer Vision for AAC
219(4)
8.2.1 Gesture Recognition
219(3)
8.2.2 Dysarthric Speech Recognition
222(1)
8.2.3 Sign Language Recognition
223(1)
8.3 AAC for Individuals With Visual Impairments
223(3)
8.3.1 The Person-Centered Multimedia Computing Paradigm
224(2)
8.4 The Social Interaction Assistant (SIA)
226(2)
8.4.1 System Description
226(1)
8.4.2 Person-Centeredness in the SIA
226(2)
8.5 Batch Mode Active Learning for Person Recognition
228(7)
8.5.1 Batch Mode Active Learning: An Introduction
228(3)
8.5.2 BMAL for Person Recognition in the SIA
231(2)
8.5.3 Person-Centered BMAL for Face Recognition
233(2)
8.6 Conformal Predictions for Multimodal Person Recognition
235(5)
8.6.1 Conformal Predictions: An Introduction
235(3)
8.6.2 Conformal Predictions for Person Recognition in the SIA
238(1)
8.6.3 Person-Centered Recognition Using the CP Framework
239(1)
8.7 Topic Models for Facial Expression Recognition
240(3)
8.7.1 Facial Expression Recognition in the SIA
240(2)
8.7.2 Person-Centered Facial Expression Recognition
242(1)
8.8 Conclusion and Discussion
243(6)
Acknowledgments
243(1)
References
243(6)
9 Computer Vision for Lifelogging
249(34)
Peng Wang
Lifeng Sun
Alan F. Smeaton
Cathal Gurrin
Shiqiang Yang
9.1 Introduction and Background
250(6)
9.1.1 Lifelogging in General
250(4)
9.1.2 Typical Applications in Assistive Living
254(2)
9.2 Semantic Indexing of Visual Lifelogs: A Static View
256(4)
9.3 Utilizing Contextual Semantics: A Dynamic View
260(9)
9.3.1 Modeling Global and Local Occurrence Patterns
260(5)
9.3.2 Attribute-Based Everyday Activity Recognition
265(4)
9.4 Interacting With Visual Lifelogs
269(3)
9.5 Conclusion and Future Issues
272(11)
Acknowledgments
275(1)
References
276(7)
10 Computational Analysis of Affect, Personality, and Engagement in Human-Robot Interactions
283(36)
Oya Celiktutan
Evangelos Sariyanidi
Hatice Gunes
10.1 Introduction
284(2)
10.2 Affective and Social Signal Processing
286(10)
10.2.1 Emotion
286(4)
10.2.2 Personality
290(4)
10.2.3 Engagement
294(2)
10.3 Two Case Studies
296(13)
10.3.1 Automatic Emotion Recognition
297(6)
10.3.2 Automatic Personality Prediction
303(6)
10.4 Conclusion and Discussion
309(10)
Acknowledgments
312(1)
References
312(7)
11 On Modeling and Analyzing Crowds From Videos
319(18)
Nicola Conci
Niccolo Bisagno
Andrea Cavallaro
11.1 Introduction
320(1)
11.2 Crowd Models
321(6)
11.2.1 The Flow of Human Crowds
321(1)
11.2.2 Continuum Crowd Model
322(1)
11.2.3 Distributed Behavioral Model
323(2)
11.2.4 Reciprocal Velocity Obstacles
325(2)
11.3 Algorithms and Applications
327(6)
11.3.1 Crowd Motion Segmentation
327(2)
11.3.2 Crowd Density Estimation
329(1)
11.3.3 People Counting
330(2)
11.3.4 Detecting Groups
332(1)
11.3.5 Anomaly Detection
332(1)
11.4 Concluding Remarks
333(4)
References
333(4)
12 Designing Assistive Tools for the Market
337(26)
Manuela Chessa
Nicoletta Noceti
Chiara Martini
Fabio Solari
Francesca Odone
12.1 Introduction
338(1)
12.2 The State of the Art
339(3)
12.3 A Study Case
342(16)
12.3.1 The Sensorized Apartment
343(3)
12.3.2 Evaluating the Motility of Patients
346(1)
12.3.3 Our Monitoring System
347(11)
12.4 Discussion
358(5)
Acknowledgments
360(1)
References
360(3)
Index 363
Marco Leo received an Honours Degree in Computer Science Engineering from the University of Salento (Italy) in 2001. Currently he is a Researcher at the National Research Council of Italy. His main research interests are in the fields of image and signal processing and analysis, computer vision, pattern recognition, neural networks, graphical models, linear and non-linear transformation (Fourier, Wavelet, ICA, kernels functions). He participated in a number of national and international research projects focusing on assistive technologies, automatic video surveillance of indoor and outdoor environments, human attention monitoring, real-time event detection in sport contexts and non-destructive inspection of aircraft components. He is author of more than 100 papers in national and international journals, and conference proceedings. He is also a co-author of three international patents on visual systems for event detection in sport contexts. Giovanni Maria Farinella obtained a degree in Computer Science (egregia cum laude) from the University of Catania, Italy, in 2004. He joined as Internal Member of the IPLAB Research Group at University of Catania in 2005. He also became an Associate Member of the Computer Vision and Robotics Research Group at University of Cambridge in 2006. He was awarded a Doctor of Philosophy (Computer Vision) from the University of Catania in 2008. He is currently a Researcher at the Department of Mathematics and Computer Science, University of Catania, Italy. His research interests lie in the fields of Computer Vision, Image Analysis, Computer Graphics, Pattern Recognition and Machine Learning. Giovanni Maria Farinella founded (in 2006) and currently directs the International Computer Vision Summer School.
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