Anomaly Detection Techniques in Mobile App Usage Data among Older Adults
Athanasios I. Kyritsis, Michel Deriaz, Dimitri Konstantas · 2018
We are living in an era of demographic ageing, and new technologies that support independent living are constantly being created. In this context, more and more mobile applications are developed for this target group. In this paper, we are presenting a multidimensional application that targets older adults. We are monitoring the usage of all different aspects of the app, the amount of daily activity in the form of daily steps and the resting time throughout the day from a connected bracelet the user is wearing. Data amounting to 402 user-days of 6 different users are collected. A set of different datasets are manufactured, and various anomaly detection techniques are employed to identify the abnormalities in the datasets. The results demonstrate that clustering can be of use to detect anomalies in the older adults' patterns that could be the trigger of appropriate actions, like informing family members or professional caregivers.