Abstract
Rapid advances in information and sensor technology have led to the development of tools and methods for personalized health monitoring. These techniques support timely and efficient healthcare services by tracking the vital signs, detecting physiological changes and predicting health risks. In this paper, we propose an integrated system to monitor the wellness condition of elderly. This system is conceptualized to provide a computer-aided decision support for clinicians and community nurses, by means of which they can easily monitor and analyze an elderly's overall activity and vital signs using a wearable wellness tracker and an all-in-one satiation-based monitoring device, offering an efficient solution with a reduction in time cost and human error. We design a data-preparing scheme for acquiring data and processing data from multiple monitoring devices, and propose a personalized scheme for forecasting the elderly's one-day-ahead wellness condition via data integration and statistical learning. We conduct a pilot study at a nursing home in Hong Kong to demonstrate the implementation of the proposed system. The proposed forecasting scheme is validated by the collected data.
| Original language | English |
|---|---|
| Pages (from-to) | 35558-35567 |
| Number of pages | 10 |
| Journal | IEEE Access |
| Volume | 6 |
| DOIs | |
| Publication status | Published - 20 Jun 2018 |
| Externally published | Yes |
Bibliographical note
The authors would like to thank Dr. Inez Zwetsloot of City University of Hong Kong for helpful discussion on this work.Funding
This work was supported in part by the RGC Theme-Based Research Scheme under Grant T32-102/14-N and in part by the National Natural Science Foundation of China under Grant 71420107023.
Keywords
- Classification
- data mining
- health monitoring system
- smart elderly care
- wellness forecasting
