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Implementing a Personalized Health Monitoring System and
controlling Data Quality in Elder Care Centres in Hong Kong
Speaker: I.M. Zwetsloot, Department of Systems Engineering and Engineering Management, City University of Hong Kong
The objective of this talk is twofold; first, I will give an overview of a pilot study focussed on implementing a data-driven personalized health monitoring system in elder care centres in Hong Kong. Second, I will dive into the statistical process control techniques developed to monitor data quality in this project.
Rapid advances in information and sensor technology have led to the
development of tools and methods for data-driven individual health monitoring. These techniques support elderly health management by tracking vital signs and detecting change in life style indicators. Three pilot studies were conducted to test and demonstrate the implementation of electronic wearable devices and an all-in- one station-based health-monitoring device at the community level in Hong Kong. Vital
signs and life-style indicators of elderly people recruited from nursing homes and day-care centres were collected over three month periods. Preliminary analysis of the collected data provided insights into the characteristics of vital signs of the elderly from these centres, which could bring benefits to the management of eldercare services.
In the second part of this talk, I present a system to monitoring data quality. We consider data from the all-in- one station-based health-monitoring device that collected the elderly’s daily vital signs. Due to the nature of the data both the sample sizes as well as the number of measured variables varies over time. We develop a new multivariate control chart to monitor the data quality. This new method, based on
the well-known Hoteling T-square control chart, has the ability to monitor data with varying sample sizes and varying number of parameters effectively. We implement the new method using the data on elderly's vital signs and show how it is used toimprove data quality on a daily basis.
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