Healthcare sensor networks (HSNs) now offer the possibility to continuously monitor human activity and physiological signals in a mobile environment. Such sensor networks may be able to reduce the strain on the present healthcare workforce by providing new autonomous monitoring services ranging from simple user-reminder systems to more advanced monitoring agents for preventive, diagnostic, and rehabilitative purposes. Potential services include reminding people to take their medication, providing early warning for the onset of heart attacks or epileptic seizures, and monitoring a child's physical activity in order to assess their growth and mental development. Healthcare Sensor Networks: Challenges Toward Practical Implementation discusses the fundamental concepts in designing and building such networks. It presents the latest developments in HSNs, explores applications of the technology, and provides insights into practical design and deployment challenges. Bringing together contributions from international experts in the field, the book highlights the key areas that require further research for HSNs to become a technological and commercially viable reality. The first part of the book concentrates on the engineering challenges, covering new biosensors, energy harvesting techniques, new wireless communication methods, and novel security approaches. Building from single sensing devices to networked sensing systems, the second part of the book looks at various health applications of HSNs. It addresses the human-centric requirements that should be considered in the design of HSN technologies-cost, portability, functionality, and user acceptance-and demonstrates how engineering compromises must be made in HSN solutions. A useful and timely resource for researchers, postgraduate students, and engineers looking for innovative solutions in healthcare, this book will also be of interest to medical and allied he

As in many other fields, biomedical engineers benefit from the use of computational intelligence (CI) tools to solve complex and non-linear problems. The benefits could be even greater if there were scientific literature that specifically focused on the biomedical applications of computational intelligence techniques. The first comprehensive field-specific reference, Computational Intelligence in Biomedical Engineering provides a unique look at how techniques in CI can offer solutions in modelling, relationship pattern recognition, clustering, and other problems particular to the field. The authors begin with an overview of signal processing and machine learning approaches and continue on to introduce specific applications, which illustrate CI's importance in medical diagnosis and healthcare. They provide an extensive review of signal processing techniques commonly employed in the analysis of biomedical signals and in the improvement of signal to noise ratio. The text covers recent CI techniques for post processing ECG signals in the diagnosis of cardiovascular disease and as well as various studies with a particular focus on CI's potential as a tool for gait diagnostics. In addition to its detailed accounts of the most recent research, Computational Intelligence in Biomedical Engineering provides useful applications and information on the benefits of applying computation intelligence techniques to improve medical diagnostics.

As in many other fields, biomedical engineers benefit from the use of computational intelligence (CI) tools to solve complex and non-linear problems. The benefits could be even greater if there were scientific literature that specifically focused on the biomedical applications of computational intelligence techniques. The first comprehensive field-specific reference, Computational Intelligence in Biomedical Engineering provides a unique look at how techniques in CI can offer solutions in modelling, relationship pattern recognition, clustering, and other problems particular to the field. The authors begin with an overview of signal processing and machine learning approaches and continue on to introduce specific applications, which illustrate CI's importance in medical diagnosis and healthcare. They provide an extensive review of signal processing techniques commonly employed in the analysis of biomedical signals and in the improvement of signal to noise ratio. The text covers recent CI techniques for post processing ECG signals in the diagnosis of cardiovascular disease and as well as various studies with a particular focus on CI's potential as a tool for gait diagnostics. In addition to its detailed accounts of the most recent research, Computational Intelligence in Biomedical Engineering provides useful applications and information on the benefits of applying computation intelligence techniques to improve medical diagnostics.

Healthcare sensor networks (HSNs) now offer the possibility to continuously monitor human activity and physiological signals in a mobile environment. Such sensor networks may be able to reduce the strain on the present healthcare workforce by providing new autonomous monitoring services ranging from simple user-reminder systems to more advanced monitoring agents for preventive, diagnostic, and rehabilitative purposes. Potential services include reminding people to take their medication, providing early warning for the onset of heart attacks or epileptic seizures, and monitoring a child's physical activity in order to assess their growth and mental development. Healthcare Sensor Networks: Challenges Toward Practical Implementation discusses the fundamental concepts in designing and building such networks. It presents the latest developments in HSNs, explores applications of the technology, and provides insights into practical design and deployment challenges. Bringing together contributions from international experts in the field, the book highlights the key areas that require further research for HSNs to become a technological and commercially viable reality. The first part of the book concentrates on the engineering challenges, covering new biosensors, energy harvesting techniques, new wireless communication methods, and novel security approaches. Building from single sensing devices to networked sensing systems, the second part of the book looks at various health applications of HSNs. It addresses the human-centric requirements that should be considered in the design of HSN technologies-cost, portability, functionality, and user acceptance-and demonstrates how engineering compromises must be made in HSN solutions. A useful and timely resource for researchers, postgraduate students, and engineers looking for innovative solutions in healthcare, this book will also be of interest to medical and allied he

Healthcare costs around the globe are on the rise, creating a strong need for new ways of assisting the requirements of the healthcare system. Besides applications in other areas, neural networks have naturally found many promising applications in the health and medicine areas. ""Neural Networks in Healthcare: Potential and Challenges"" presents interesting and innovative developments from leading experts and scientists working in health, biomedicine, biomedical engineering, and computing areas. This book covers many important and state-of-the-art applications in the areas of medicine and healthcare, including: cardiology, electromyography, electroencephalography, gait and human movement, therapeutic drug monitoring for patient care, sleep apnea, and computational fluid dynamics areas. ""Neural Networks in Healthcare: Potential and Challenges"" is a useful source of information for researchers, professionals, lecturers, and students from a wide range of disciplines. Readers of this book will be able to use the ideas for further research efforts in this very important and highly multidisciplinary area.

Recent years have seen many new developments in computational intelligence (CI) techniques and, consequently, this has led to an exponential increase in the number of applications in a variety of areas, including: engineering, finance, social and biomedical. In particular, CI techniques are increasingly being used in biomedical and human movement areas because of the complexity of the biological systems, as well as the limitations of the existing quantitative techniques in modelling. ""Computational Intelligence for Movement Sciences: Neural Networks and Other Emerging Techniques"" contains information regarding state-of-the-art research outcomes and cutting-edge technology from leading scientists and researchers working on various aspects of the human movement. Readers of this book will gain an insight into this field, as well as access to pertinent information, which they will be able to use for continuing research in this area.