Examines various speech technologies deployed in healthcare service robots to maximize the robot's ability to interpret user input. Demonstrates how robot anthropomorphic features and etiquette in behavior promotes user-positive emotions, acceptance of robots, and compliance with robot requests. Analyzes how multimodal medical-service robots and other cyber-physical systems can reduce mistakes and mishaps in the operating room. Evaluates various input methods for improving acceptance of robots in the older adult population. Presents case studies of cognitively and socially engaging robots in the long-term care setting for helping older adults with activities of daily living and in the pediatric setting for helping children with autism spectrum conditions and metabolic disorders. Speech and Automata in Health Care forges new ground by closely analyzing how three separate disciplines - speech technology, robotics, and medical/surgical/assistive care - intersect with one another, resulting in an innovative way of diagnosing and treating both juvenile and adult illnesses and conditions. This includes the use of speech-enabled robotics to help the elderly population cope with common problems associated with aging caused by the diminution in their sensory, auditory and motor capabilities. By examining the emerging nexus of speech, automata, and health care, the authors demonstrate the exciting potential of automata, both speech-driven and multimodal, to affect the healthcare delivery system so that it better meets the needs of the populations it serves. This book provides both empirical research findings and incisive literature reviews that demonstrate some of the more novel uses of speech-enabled and multimodal automata in the operating room, hospital ward, long-term care facility, and in the home. Studies backed by major universities, research institutes, and by EU-funded collaborative projects are debuted in this volume. This volume provides a wealth of timely material for industrial engineers, speech scientists, computational linguists, and for signal processing and intelligent systems design experts. Topics include: Spoken Interaction with Healthcare Robots Service Robot Feature Effects on Patient Acceptance/Emotional Response Designing Embodied and Virtual Agents for the Operating Room The Emerging Role of Robotics for Personal Health Management in the Older-Adult Population Why Input Methods for Robots that Serve the Older Adult Are Critical for Usability Socially and Cognitively Engaging Robots in the Long-Term Care Setting Voice-Enabled Assistive Robots for Managing Autism Spectrum Conditions ASR and TTS for Voice-Controlled Robot Interactions in Treating Children with Metabolic Disorders

This book offers an overview of some recent advances in the Computational Bioacoustics methods and technology. In the focus of discussion is the pursuit of scalability, which would facilitate real-world applications of different scope and purpose, such as wildlife monitoring, biodiversity assessment, pest population control, and monitoring the spread of disease transmitting mosquitoes. The various tasks of Computational Bioacoustics are described and a wide range of audio parameterization and recognition tasks related to the automated recognition of species and sound events is discussed. Many of the Computational Bioacoustics methods were originally developed for the needs of speech, audio, or image processing, and afterwards were adapted to the requirements of automated acoustic recognition of species, or were elaborated further to address the challenges of real-world operation in 24/7 mode. The interested reader is encouraged to follow the numerous references and links to web resources for further information and insights. This book is addressed to Software Engineers, IT experts, Computer Science researchers, Bioacousticians, and other practitioners concerned with the creation of new tools and services, aimed at enhancing the technological support to Computational Bioacoustics applications. STTM, Speech Technology and Text Mining in Medicine and Health Care This series demonstrates how the latest advances in speech technology and text mining positively affect patient healthcare and, in a much broader sense, public health at large. New developments in text mining methods have allowed health care providers to monitor a large population of patients at any time and from any location. Employing advanced summarization techniques, patient data can be readily extracted from extensive clinical documents in electronic health records and immediately made available to the physician. These same summarization techniques can also aid the healthcare provider in extracting from the large corpora of medical literature the relevant information for treating the patient. The series topics include the design and acceptance of speech-enabled robots that assist in the operating room, studies of signal processing and acoustic modeling for speech and communication disorders, advanced statistical speech enhancement methods for creating synthetic voice, and technologies for addressing speech and language impairments. Titles in the Series consist of both authored books and edited contributions. All authored books and contributed works are peer-reviewed. The Series is for speech scientists and speech engineers, machine learning experts, biomedical engineers, medical speech pathologists, linguists, and healthcare professionals

Today, autonomous robots are used in a rather limited range of applications such as exploration of inaccessible locations, cleaning floors, mowing lawns etc. However, ongoing hardware improvements (and human fantasy) steadily reveal new robotic applications of significantly higher sophistication. For such applications, the crucial bottleneck in the engineering process tends to shift from physical boundaries to controller generation. As an attempt to automatize this process, Evolutionary Robotics has successfully been used to generate robotic controllers of various types. However, a major challenge of the field remains the evolution of truly complex behavior. Furthermore, automatically created controllers often lack analyzability which makes them useless for safety-critical applications. In this book, a simple controller model based on Finite State Machines is proposed which allows a straightforward analysis of evolved behaviors. To increase the model's evolvability, a procedure is introduced which, by adapting the genotype-phenotype mapping at runtime, efficiently traverses both the behavioral search space as well as (recursively) the search space of genotype-phenotype mappings. Furthermore, a data-driven mathematical framework is proposed which can be used to calculate the expected success of evolution in complex environments.

Interactive mobile technologies have now become the core of many—if not all—fields of society. Not only do the younger generation of students expect a mobile working and learning environment, but also the new ideas, technologies and solutions introduced on a nearly daily basis also boost this trend. Discussing and assessing key trends in the mobile field were the primary aims of the 11th International Conference on Interactive Mobile Communication, Technologies and Learning (IMCL2017), which was held in Thessaloniki from 30 November to 01 December 2017. Since being founded in 2006, the conference has been devoted to new approaches in interactive mobile technologies, with a focus on learning. The IMCL conferences have in the meanwhile become a central forum of the exchange of new research results and relevant trends, as well as best practices. This book contains papers in the fields of: Future Trends and Emerging Mobile Technologies Design and Development of Mobile Learning Apps and Content Mobile Games—Gamification and Mobile Learning Adaptive Mobile Environments Augmented Reality and Immersive Applications Tangible, Embedded and Embodied Interaction Interactive Collaborative and Blended Learning Digital Technology in Sports Mobile Health Care and Training Multimedia Learning in Music Education 5G Network Infrastructure Case Studies Real-World Experiences The content will appeal to a broad readership, including policymakers, academics, educators, researchers in pedagogy and learning theory, school teachers, the learning industry, further education lecturers, etc.

Degeneration of nerve cells that control cognitive, speech, and language processes leading to linguistic impairments at various levels, from verbal utterances to individual speech sounds, could indicate signs of neurological, cognitive and psychiatric disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS), dementias, depression, autism spectrum disorder, schizophrenia, etc. Currently, these disorders are diagnosed using specific clinical diagnostic criteria and neuropsychological examinations. However, speech-based biomarkers could potentially offer many advantages over current clinical standards. In addition to being objective and naturalistic, they can also be collected remotely with minimal instruction and time requirements. Furthermore, Machine Learning algorithms developed to build automated diagnostic models using linguistic features extracted from speech could aid diagnosis of patients with probable diseases from a group of normal population. To ensure that speech-based biomarkers are providing accurate measurement and can serve as effective clinical tools for detecting and monitoring disease, speech features extracted and analyzed must be systematically and rigorously evaluated. Different machine learning architectures trained to classify different types of disordered speech must also be rigorously tested and systematically compared.

This book presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. It is intended to provide a unique compendium of current and emerging machine learning paradigms for healthcare informatics, reflecting the diversity, complexity, and depth and breadth of this multi-disciplinary area.

Signals and systems enjoy wide application in industry and daily life, and understanding basic concepts of the subject area is of importance to undergraduates majoring in engineering. With rigorous mathematical deduction, this introductory text book is helpful for students who study communications engineering, electrical and electronic engineering, and control engineering. Additionally, supplementary materials are provided for self-learners.

Advances in Ubiquitous Computing: Cyber-Physical Systems, Smart Cities and Ecological Monitoring debuts some of the newest methods and approaches to multimodal user-interface design, safety compliance, formal code verification and deployment requirements, as they pertain to cyber-physical systems, smart homes and smart cities, and biodiversity monitoring. In this anthology, the authors assiduously examine a panoply of topics related to wireless sensor networks. These topics include interacting with smart-home appliances and biomedical devices, designing multilingual speech recognition systems that are robust to vehicular, mechanical and other noises common to large metropolises, and an examination of new methods of speaker recognition to control for the emotion-state of the speaker, which can easily impede speaker verification over a wireless medium. This volume recognizes that any discussion of pervasive computing in smart cities must not end there, as the perilous effects of climate change proves that our lives are not circumscribed by the geographically sculpted boundaries of cities, counties, countries, or continents. Contributors address present and emerging technologies of scalable biodiversity monitoring: pest control, disease transmission, environmental monitoring, and habitat preservation. The need to collect, store, process, and interpret vast amounts of data originating from sources spread over large areas and for prolonged periods of time requires immediate data storage and processing, reliable networking, and solid communication infrastructure, along with intelligent data analysis and interpretation methods that can resolve contradictions and uncertainty in the data—all of which can be bolstered by modern advances in ubiquitous computing. - Examines the history, scope and advances in ubiquitous computing, including threats to wildlife, tracking of disease, smart cities and Wireless Sensor Networks - Discusses user interface design, implementation and deployment of cyber-physical systems, such as wireless sensor networks, Internet of Things devices, and other networks of physical devices that have computational capabilities and reporting devices - Covers the need for improved data sharing networks

PAAMS, the International Conference on Practical Applications of Agents and Multi-Agent Systems is an international yearly stage to present, to discuss, and to disseminate the latest advances and the most important outcomes related to real-world applications. It provides a unique opportunity to bring multi-disciplinary experts, academics and practitioners together to exchange their experience in the development of Agents and Multi-Agent Systems. This volume presents the papers that have been accepted for the 2010 edition. These articles capture the most innovative results and this year’s advances. Each paper has been reviewed by three different reviewers, from an international com-mittee composed of 82 members from 26 different countries. From the 66 submissions received, 19 were selected for full presentation at the conference, and 14 were accepted as short papers. Moreover, PAAMS'10 incorporated special ses-sions and workshops to complement the regular program, which included 85 ac-cepted papers.

• Includes Text Mining and Natural Language Processing Methods for extracting information from electronic health records and biomedical literature. • Analyzes text analytic tools for new media such as online forums, social media posts, tweets and video sharing. • Demonstrates how to use speech and audio technologies for improving access to online content for the visually impaired. Text Mining of Web-Based Medical Content examines various approaches to deriving high quality information from online biomedical literature, electronic health records, query search terms, social media posts and tweets. Using some of the latest empirical methods of knowledge extraction, the authors show how online content, generated by both professionals and laypersons, can be mined for valuable information about disease processes, adverse drug reactions not captured during clinical trials, and tropical fever outbreaks. Additionally, the authors show how to perform infromation extraction on a hospital intranet, how to build a social media search engine to glean information about patients' own experiences interacting with healthcare professionals, and how to improve access to online health information. This volume provides a wealth of timely material for health informatic professionals and machine learning, data mining, and natural language researchers. Topics in this book include: • Mining Biomedical Literature and Clinical Narratives • Medication Information Extraction • Machine Learning Techniques for Mining Medical Search Queries • Detecting the Level of Personal Health Information Revealed in Social Media • Curating Layperson’s Personal Experiences with Health Care from Social Media and Twitter • Health Dialogue Systems for Improving Access to Online Content • Crowd-based Audio Clips to Improve Online Video Access for the Visually Impaired • Semantic-based Visual Information Retrieval for Mining Radiographic Image Data • Evaluating the Importance of Medical Terminology in YouTube Video Titles and Descriptions