Biomedical sensors and smart sensing a beginner's guide /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Panja, Ayan
Otros Autores: Mukherjee, Amartya, Dey, Nilanjan, 1984-
Formato: Electrónico eBook
Idioma:Inglés
Publicado: London, UK : Academic Press, 2022.
Temas:
Biosensors.
Intelligent sensors.
Biosensing Techniques
Biocapteurs.
Capteurs intelligents.
Biosensors
Intelligent sensors
Acceso en línea:Texto completo
Tabla de Contenidos:
  • Intro
  • Biomedical Sensors and Smart Sensing: A Beginner's Guide
  • Copyright
  • Contents
  • Author's biographies
  • Preface
  • Acknowledgments
  • About the book
  • Chapter 1: Introduction
  • 1.1. Biomedical sensors and system overview
  • 1.2. Physical characteristics
  • 1.2.1. Magnetic sensing
  • 1.2.2. Electrical sensing
  • 1.2.3. Acoustic sensing
  • 1.3. System and signal
  • 1.3.1. Measurement
  • 1.3.2. Biopotentials
  • 1.4. Sensor characteristics
  • 1.4.1. Sensitivity of the sensor
  • 1.4.2. Linearity
  • 1.4.3. Sensing errors
  • 1.5. Biopotential signal monitoring and biosensors
  • 1.6. Conclusion
  • References
  • Chapter 2: Sensing and data gathering methodology
  • 2.1. Signals and noise of sensors
  • 2.1.1. Various classes of noise
  • 2.1.2. Sensing and measurement
  • 2.1.3. Calibration and error scenarios
  • 2.2. Flow sensing and measurement technique
  • 2.3. Ultrasound-based blood flow sensing
  • 2.4. Force-sensing measurement
  • 2.5. Foot force measurement using smart shoe
  • 2.6. ECG sensing and measurement
  • 2.6.1. Electrocardiogram systems
  • 2.6.2. Calibration and lead
  • 2.6.3. Real-time IoT-based ECG sensing application
  • 2.6.4. Heart disease prediction mechanism
  • 2.7. EEG fundamentals
  • 2.8. EEG signal analysis and classification
  • 2.9. Ultrasound sensing for tissues and fetal growth observation
  • 2.9.1. Obstetrical sonography
  • 2.10. Conclusion
  • References
  • Chapter 3: Medical signal processing
  • 3.1. Overview
  • 3.2. Time series analysis
  • 3.2.1. Signal overview
  • 3.2.2. Some approaches
  • 3.2.2.1. Moving average
  • 3.2.2.2. Autoregressive moving average
  • 3.2.2.3. ARIMA
  • 3.3. Multiscale signal processing
  • 3.3.1. Various signal processing models
  • 3.4. Biomedical imaging and analysis
  • 3.4.1. Magnetic resonance imaging
  • 3.4.2. Computed tomography
  • 3.4.3. Ultrasound-based diagnosis.
  • 3.4.4. Abdominal imaging for computer-aided diagnosis
  • 3.5. Image enhancement
  • 3.6. Image classification technique
  • 3.7. Wearable and implantable technologies
  • 3.8. Conclusion
  • References
  • Chapter 4: Sensor data analysis
  • 4.1. Machine learning preliminaries
  • 4.2. Feature engineering
  • 4.3. Perceptron learning
  • 4.4. Application of machine learning on ECG data
  • 4.4.1. ECG raw data classification
  • 4.4.2. ECG image classification
  • 4.4.3. ECG sound segmentation
  • 4.5. Ambient medical data processing
  • 4.5.1. Wearable healthcare
  • 4.5.2. Wireless body area network
  • 4.5.2.1. WBAN implementation for patient monitoring
  • 4.6. Conclusion
  • References
  • Chapter 5: IoT and medical cyberphysical systems road map
  • 5.1. Introduction
  • 5.2. Ubiquitous sensing paradigm
  • 5.2.1. Internet of medical things
  • 5.2.2. Smart pill technology
  • 5.3. IEEE 1918.1 tactile IoT
  • 5.4. Functional architecture
  • 5.5. Applications and services
  • 5.5.1. Industrial automation
  • 5.5.2. Robotics and motion planning
  • 5.5.3. Healthcare applications
  • 5.5.4. Augmented, virtual, and mixed reality applications
  • 5.6. 5G and healthcare
  • 5.7. Wi-fi and the femtocell
  • 5.8. Software-defined networks
  • 5.9. Slicing under SDN
  • 5.10. Drone as a component of healthcare and MCPS
  • 5.11. UAV-assisted COVID-19 monitoring
  • 5.12. The use of sensors and IoT infrastructure to fight against COVID-19
  • 5.12.1. Smart thermometer
  • 5.12.2. Networks and cloud robots
  • 5.12.3. Autonomous vehicles
  • 5.13. Drone delivery in COVID-19 situation
  • 5.14. COVID-19 prediction modeling
  • 5.15. Conclusion
  • References
  • Chapter 6: Smart perishable food and medicine management overview
  • 6.1. Introduction
  • 6.2. Food and medical supply chain perspective
  • 6.2.1. Food supply chain
  • 6.2.2. Medicine supply chain management.
  • 6.3. Internet of things concepts
  • 6.4. Literature survey
  • 6.5. The ecosystem of the IoT-based system
  • 6.5.1. IoT solution for medicine
  • 6.5.2. Hybrid vehicular DTN-based IoT methodology
  • 6.5.3. DTN-based IoT approach
  • 6.5.3.1. Direct contact method
  • 6.5.3.2. Epidemic routing methods
  • 6.5.3.3. Location-based routing mechanism
  • 6.6. Analysis of different methodologies
  • 6.6.1. Message delivery ratio comparison
  • 6.6.2. Buffer usage comparison
  • 6.6.3. Comparison of quality of services
  • 6.7. Conclusion
  • References
  • Chapter 7: Overview of data gathering and cloud computing in healthcare
  • 7.1. Introduction
  • 7.2. Wireless sensor network
  • 7.3. Transmission methods
  • 7.3.1. Unicast
  • 7.3.2. Multicast
  • 7.3.3. Broadcast
  • 7.3.4. Convergecast
  • 7.4. Cloud computing
  • 7.5. Medical cloud application
  • 7.5.1. Information management and sharing
  • 7.5.2. Medical support system
  • 7.5.3. Clinical analytics
  • 7.6. Issues in healthcare cloud
  • 7.7. Conclusion
  • References
  • Index.

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