Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring.

Area of Science:

• Biomedical Engineering
• Health Informatics
• Machine Learning in Healthcare

Background:

• Telehealth and remote patient monitoring (RPM) have become crucial since the pandemic, offering accessible and cost-effective patient care.
• Existing systems often lack comprehensive activity and vital sign tracking during daily routines.
• The need for integrated solutions to monitor patient health continuously and remotely is growing.

Purpose of the Study:

• To propose an Intelligent Remote Patient Activity Tracking System (IRPATS) for comprehensive patient monitoring.
• To develop an Internet of Things (IoT)-enabled device capable of tracking activities, vital signs, and breathing patterns.
• To utilize machine learning for analyzing patient data and assessing respiratory health.

Main Methods:

• Design of an IoT-enabled health monitoring device with attached sensors.
• Implementation of machine learning models to identify diverse patient activities (e.g., running, sleeping, walking).
• Integration of algorithms to analyze vital signs (body temperature, heart rate) and breathing patterns during activities.
• Development of a web application for data visualization and tracking.

Main Results:

• The system successfully monitors various patient activities and associated vital signs.
• Machine learning models are employed to differentiate between activities and analyze respiratory patterns.
• Current models can detect coughs and healthy breathing, with potential for broader respiratory health analysis.
• A functional web application facilitates the tracking of uploaded patient data.

Conclusions:

• The proposed Intelligent Remote Patient Activity Tracking System offers a novel approach to continuous, remote health monitoring.
• Integration of IoT and machine learning enhances the ability to track patient activities and physiological data.
• This system has the potential to improve patient care quality and enable early detection of health issues, particularly respiratory conditions.