COMPUTER VISION FOR ASSISTIVE HEALTHCARE PREFACE

This book reflects the importance of considering the "real world" social impact of technology alongside the fundamental goals of basic R& D. Computer vision is an area of scientific and technological development that will continue to have a profound impact on society. It will redefine the way that information technology intersects and interfaces with medicine and other disciplines, and it will play a key role in the care of an aging population and in improving the quality of life in our modern society. The main aim of this book is to present the state of the art in the context of Computer Vision for Assistive Healthcare. The different chapters present the latest progress in this domain and discuss novel ideas in the field. In addition to the technologies used, emphasis is given to the definition of the problems, the available benchmark databases, the evaluation protocols, and procedures. Chapter 1, by Zhigang Zhu et al., presents a vision-based assistive indoor localization approach with various techniques in different stages for helping the visually impaired to localize in and navigate through indoor environments. Unlike other computer vision research, whose problems are already well-defined and formalized by the community and whose major tasks are to apply their developed algorithms to standard datasets by tuning the parameter of models and evaluating the performance, this work studies the navigation needs of the visually impaired, and then helps us develop techniques in data collection, model building, localization, and user interfaces in both pre-journey planning and real-time assistance. Chapter 2, by Corneliu Florea et al., approaches computer vision solutions to the diagnostic aid of several cognitive-affective psychiatric disorders. It reviews contributions that investigate cognitive impairments that appear at all stages of human development: from childhood and cognitive accumulation (autism, dyslexia), through adulthood and trauma-related cognitive degradations (such as phobias and PTSD), and ending with the ultimate degenerative cognitive degradations induced by dementia. Chapter 3, by Antonio Frisoli et al., describes a computer vision-based robot-assisted system used in neurorehabilitation of post-stroke patients, which allows the subjects to reach and grasp objects in a defined workspace. In particular, a novel RGB-D-based algorithm used to track generic unknown objects in real time is proposed. The novelty of the proposed tracking algorithm comes from combining different features to achieve object recognition and tracking Chapter 4, by Nassir Navab et al., outlines how computer vision can support the surgeon during an intervention using the example of surgical instrument tracking in retinal microsurgery, which incorporates challenges and requirements that are common when using this technique in various medical applications. In particular, how to derive algorithms for simultaneous tool tracking and pose estimation based on random forests and how to increase robustness to problems associated with retinal microsurgery images, such as strong illumination variations and high noise levels, is shown. Chapter 5, by Qiuhong Ke et al., focuses on the gesture recognition task for HMI and introduces current deep learning methods that have been used for human motion analysis and RGB-D-based gesture recognition. More specifically, it briefly introduces the convolutional neural networks (CNNs), and then presents several deep learning frameworks based on CNNs that have been used for gesture recognition by using RGB, depth, and skeleton sequences. Chapter 6, by Sara Colantonio et al., offers a brief survey of existing, vision-based monitoring solutions for personalized health care and wellness, and introduces the Wize Mirror,