Geriatric Care Management System Powered by the IoT and Computer Vision Techniques.

Area of Science:

• Geriatric Care Technology
• Digital Health Innovations
• Artificial Intelligence in Healthcare

Background:

• Traditional geriatric care often involves manual bioparameter measurement and paper-based plans, leading to inefficiencies and errors.
• Inconsistent monitoring and record-keeping in elderly care can delay the identification and resolution of health issues.

Purpose of the Study:

• To develop an integrated geriatric care management system leveraging advanced technologies.
• To enhance the monitoring and detection of health status changes in elderly individuals.

Main Methods:

• Integration of data from wearable sensors, noncontact devices, and image recognition.
• Utilization of deep learning algorithms and the Internet of Things (IoT) for patient identification and pose analysis.
• Development of a decision tree-based model incorporating expert knowledge for automated care plan status determination.

Main Results:

• The system identifies patients and analyzes six key poses using deep learning and IoT.
• Algorithms monitor positional changes over time for early health problem detection.
• An automated decision-making model supports nursing staff by assessing nursing care plan status.

Conclusions:

• The developed system offers a technologically advanced approach to geriatric care management.
• Digitalization through integrated sensors, AI, and IoT can significantly improve the accuracy and efficiency of elderly care.
• This system supports timely interventions and enhances the overall quality of care for the elderly.