A Scalable Solution to Detect Behavior Changes of Elderly People Living Alone

Abstract

As the world population is ageing rapidly and old age comes with multiple health issues, the need for medical services is likely to increase in a couple of decades beyond the limits of the medical systems of almost any country. In response to this trend, a variety of technologies have been developed with the aim of helping older people live independently as long as possible and reduce the burden on caregivers. In this paper, we propose a solution to encode the information about the activity of the monitored person, captured by a set of binary sensors, in the form of activity maps that reflect not only the intensity, but also the spatial distribution of the activity between a set of behaviorally meaningful places. Then, we propose a method for automatic analysis of the activity maps in order to detect deviations from the previously recorded routine. We have tested the method on two public activity recognition datasets and found that the proposed solution is not only feasible, but also has several important advantages (it is low cost, scalable, adaptable, requires little expert knowledge for setup and protects the privacy of the monitored persons) that make it applicable on a large scale, including for people with low socio-economic status.

Keywords

• activity monitoring
• activities of daily life
• behaviorally meaningful places
• anomaly detection
• unobtrusive sensors
• activity maps