IoT Enabled Health Monitoring System Using Wearable Sensors for Chronic Disease Management
Session
Computer Science and Communication Engineering
Description
This paper reviews the application of IoT-enabled health monitoring systems using wearable sensors for chronic disease management, particularly focusing on Type 2 Diabetes (T2D). It explores how advanced technologies, including machine learning (ML) and virtual reality (VR), can enhance real-time health monitoring and predictive analytics. Wearable sensors collect essential health data, which is analyzed using ML algorithms to predict disease progression and improve treatment strategies. The study also considers VR’s role in enhancing patient education and self-management. Additionally, the integration of these systems with electronic health records (EHR) is examined to facilitate seamless data access for healthcare providers. Ethical considerations, such as data privacy and patient consent, are addressed to ensure compliance with regulatory standards. The review highlights the potential of IoT, ML, and VR to transform chronic disease management by advancing personalized care and improving patient outcomes. Index Terms—Type 2 Diabetes, machine learning, virtual reality, realtime health monitoring, IoT, wearable sensors.
Proceedings Editor
Edmond Hajrizi
Start Date
25-10-2024 9:00 AM
End Date
27-10-2024 6:00 PM
DOI
10.33107/ubt-ic.2024.401
Recommended Citation
Ahma, Greta; Reqica, Mirlinda; and Hajrizi, Elita, "IoT Enabled Health Monitoring System Using Wearable Sensors for Chronic Disease Management" (2024). UBT International Conference. 17.
https://knowledgecenter.ubt-uni.net/conference/2024UBTIC/CS/17
IoT Enabled Health Monitoring System Using Wearable Sensors for Chronic Disease Management
This paper reviews the application of IoT-enabled health monitoring systems using wearable sensors for chronic disease management, particularly focusing on Type 2 Diabetes (T2D). It explores how advanced technologies, including machine learning (ML) and virtual reality (VR), can enhance real-time health monitoring and predictive analytics. Wearable sensors collect essential health data, which is analyzed using ML algorithms to predict disease progression and improve treatment strategies. The study also considers VR’s role in enhancing patient education and self-management. Additionally, the integration of these systems with electronic health records (EHR) is examined to facilitate seamless data access for healthcare providers. Ethical considerations, such as data privacy and patient consent, are addressed to ensure compliance with regulatory standards. The review highlights the potential of IoT, ML, and VR to transform chronic disease management by advancing personalized care and improving patient outcomes. Index Terms—Type 2 Diabetes, machine learning, virtual reality, realtime health monitoring, IoT, wearable sensors.