Mathematical Framework for Wearable Devices in the Internet of Things Using Deep Learning.

Area of Science:

• Medical Technology
• Internet of Things (IoT)
• Remote Health Monitoring

Background:

• The medical sector requires rapid and accurate infection identification methods, especially for remote regions.
• Existing wearable IoT devices often face challenges with communication loss, detection time, and quality.
• Current solutions lack robust mathematical and optimization strategies for reliable duplication.

Purpose of the Study:

• To develop a wearable Internet of Things (IoT) device for efficient infection detection in remote areas.
• To implement a multi-objective framework to minimize communication loss and detection wait times while improving accuracy.
• To establish a design methodology for wearable IoT devices using mathematical approaches and deep learning.

Main Methods:

• A wearable device integrated with Internet of Things (IoT) capabilities was designed.
• A multi-objective framework was employed, utilizing distinct mathematical approaches for optimization.
• State design and deep learning (DL) optimization techniques were combined to reduce detection complexity.
• Monitored data were stored on a separate IoT application platform.

Main Results:

• The developed wearable device demonstrated improved detection quality and reduced communication loss.
• The multi-objective framework and DL optimization enhanced the efficiency of the wearable technology.
• The proposed method showed superior performance compared to existing state-of-the-art techniques across five different scenarios.
• Real-time testing and IoT simulation confirmed the effectiveness of the developed system.

Conclusions:

• The wearable IoT device offers a viable solution for fast, real-time remote medical monitoring.
• The integration of a multi-objective framework and deep learning significantly advances wearable health technology.
• The proposed approach provides a robust and reproducible method for remote infection detection.