User Activity Pattern Analysis in Telecare Data

• Main Authors: Maia Angelova, Jeremy Ellman, Helen Gibson, Paul Oman, Sutharshan Rajasegarar, Ye Zhu
• Format: Article
• Language: English
• Published: IEEE 2018-01-01
• Series: IEEE Access
• Subjects: Aging care; data analytics; machine learning; statistical analysis; telecare
• Online Access: https://ieeexplore.ieee.org/document/8385090/

Telecare is the use of devices installed in homes to deliver health and social care to the elderly and infirm. The aim of this paper is to identify patterns of use for different devices and associations between them. The data were provided by a telecare call center in the North East of England. Using statistical analysis and machine learning, we analyzed the relationships between users' characteristics and device activations. We applied association rules and decision trees for the event analysis, and our targeted projection pursuit technique was used for the user-event modeling. This study reveals that there is a strong association between users' ages and activations, i.e., different age group users exhibit different activation patterns. In addition, a focused analysis on the users with mental health issues reveals that the older users with memory problems who live alone are likely to make more mistakes in using the devices than others. The patterns in the data can enable the telecare call center to gain insight into their operations and improve their effectiveness in several ways. This study also contributes to automatic analysis and support for decision making in the telecare industry.