Abstract
Growing aging population highlights the importance of managing chronic diseases. The rapid development of Internet of Things (IoT) and big data analysis makes it feasible and affordable to monitor and manage chronic diseases for caring at home. The process of chronic diseases management involves monitoring rehabilitation and recovery, tracking physiological and behavioral status, and health condition classification and diagnosis. In this paper, we proposed a framework for monitoring and management of home care elderly adults. A three-level architecture, which is IoT-Intelligent Terminal(IT)-Cloud, is established to achieve data acquisition, signal transmission, remote interaction and diagnosis. Five diagnosis methods including Decision Tree (DT), Random Forest (RF), Support Vector Machine (SVM), K Near Neighbor (KNN), and Back Process Neural Network (BPNN) are implemented to evaluate the risk of suffering from heart disease and experiments showed the accuracy is over 95%. Experimental result reveals that the data flow and remote interaction in this system are effective and primary diagnosis is validated.
| Original language | American English |
|---|---|
| Pages (from-to) | 11034-11044 |
| Number of pages | 11 |
| Journal | IEEE Sensors Journal |
| Volume | 22 |
| Issue number | 11 |
| DOIs | |
| State | Published - Jun 1 2022 |
ASJC Scopus subject areas
- Instrumentation
- Electrical and Electronic Engineering
Keywords
- Sensor system integration
- millimeter wave sensors
- sensor data processing
- sensor system networks