2020

Jansson, Johan; Hakala, Ismo

A challenge in developing an ambient activity recognition system for use in elder care is finding a balance between the sophistication of the system and a cost structure that fits within the budgets of public and private sector healthcare organisations. Much activity recognition research in the context of elder care is based on dense networks of sensors and advanced methods, such as supervised machine learning algorithms. This paper presents the data processing aspects of an activity recognition system based on a simpler, knowledge-based unsupervised approach, designed for a sparse network of sensors. By structuring sensor data management as a streaming system, we provide a simple programming model for the application logic, which facilitates building a fault-tolerant system with the potential for distributed data management within the sensor network. The system, evaluated by a public sector healthcare organisation, constitutes an example of a system that is useful and has a sustainable cost structure.