Rezaul Begg – författare

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.

Intelligent Gait Assistive Technologies: Gait Biomechanics and Machine Learning Applications in Rehabilitation and Injury Prevention brings together contemporary research and applications to show how gait biomechanics combined with machine learning can be used to develop techniques to provide safer, more mechanically efficient locomotion to individuals with significant visual, musculoskeletal, or neurological deficits. Developments in gait rehabilitation and injury prevention outlined in this book will contribute to improved quality of life for individuals with gait-related impairments, with a major contribution to medical cost savings due to reduction in falls.Researchers, engineers, and students in biomedical engineering and biomechanics will find this a welcomed reference in better understanding the role of machine learning and intelligent technologies in the advancement of gait rehabilitation and injury reduction to both impaired and healthy individuals.

Combines gait biomechanics, assistive technologies, and machine learning to showcase applications in healthcarePresents a state-of-the-art reference with exoskeletons and assistive devices and emerging techniquesIntroduces an easy-to-understand format by engineers and health professionals

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