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Wearable Telemedicine Technology for the Healthcare Industry

Product Design and Development
Sofort lieferbar | Lieferzeit: Sofort lieferbar I
ISBN-13:
9780323858106
Veröffentl:
2021
Seiten:
192
Autor:
Deepak Gupta
eBook Typ:
EPUB
eBook Format:
EPUB
Kopierschutz:
2 - DRM Adobe
Sprache:
Englisch
Beschreibung:

Wearable Telemedicine Technology for the Healthcare Industry: Product Design and Development focuses on recent advances and benefits of wearable telemedicine techniques for remote health monitoring and prevention of chronic conditions, providing real time feedback and help with rehabilitation and biomedical applications. Readers will learn about various techniques used by software engineers, computer scientists and biomedical engineers to apply intelligent systems, artificial intelligence, machine learning, virtual reality and augmented reality to gather, transmit, analyze and deliver real-time clinical and biological data to clinicians, patients and researchers.
1. Human Body Interaction Driven Wearable Technology for Vital Signal Sensing 2. HealthWare Telemedicine Technology (HWTT) Evolution Map for Healthcare 3. Blockchain: A Novel Paradigm for Secured Data Conduct in Telemedicine 4. Wearable Technology and Artificial Intelligence in Psychiatric Disorders 5. Applying wearable smart sensors for vital signs controlling of patients in epidemics 6. A Novel Compressive Sensing with Deep Learning based Disease Diagnosis Model for Smart Wearable Healthcare Devices 7. Blockchain based Secure Data Sharing Scheme using Image Steganography and Encryption Techniques for Telemedicine Applications 8. Intelligent Metaheuristic Cluster based Wearable Devices for Healthcare Monitoring in Telemedicine Systems 9. Class Imbalance Data Handling with Deep Learning based Ubiquitous Healthcare Monitoring System using Wearable Devices 10. IoT and Wearables for Detection of COVID-19 Diagnosis using Fusion based Feature Extraction with Multi-Kernel Extreme Learning Machine 11. Internet of Things and Wearables Enabled Alzheimer detection and Classification Model using Stacked Sparse Autoencoder

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