Smart Healthcare System Based on Cloud-Internet of Things and Deep Learning.

Area of Science:

• Computer Science
• Biomedical Engineering
• Artificial Intelligence

Background:

• Current wearable devices for smart healthcare have limited measurement parameters.
• Cloud servers face significant computing pressure for health data analysis.
• Existing remote diagnosis lacks individualization and personalized health insights.

Purpose of the Study:

• To propose a novel Cloud-Internet of Things (C-IOT) framework for advanced medical monitoring.
• To address limitations in current smart healthcare systems, including data processing and diagnostic personalization.
• To develop a deep learning model for accurate health status recognition and remote diagnosis.

Main Methods:

• Utilized smartphones as gateway devices for data standardization and preprocessing.
• Developed a cloud server architecture for business logic processing and health parameter analysis.
• Constructed a deep learning model based on Convolutional Neural Network (CNN) trained on volunteer health data.

Main Results:

• The proposed C-IOT framework demonstrated feasibility in experimental trials.
• The trained CNN model achieved a forecast accuracy exceeding 77.6% on a test dataset.
• The CNN model showed strong performance in recognizing various health statuses.

Conclusions:

• The developed smart healthcare system effectively assists doctors in clinical practice.
• The CNN model shows significant potential for improving the accuracy of health status diagnosis.
• The C-IOT framework offers a scalable and personalized approach to remote health monitoring.