Event-Driven Framework for Detecting Unusual Patterns in AAL Environments

An aging population has motivated research into Ambient Assisted Living (AAL) with the aim of supporting people to continue to live in their homes as they age or with chronic health problems. As part of this work, some researchers have focused on identifying and reporting daily activities of individuals at home in order to try to reduce the workload on caregivers and health professionals. Such an environment is usually monitored by non-wearable sensors that collect a vast amount of data. The use of this data requires computational methods that can process it in a reasonable time. This paper proposes an event-driven framework to detect unusual patterns in AAL environments. A Fog-Cloud paradigm and Lambda architecture are adopted as the framework to support computational solutions to deal with the volume of data, and machine learning techniques are used for recognition of daily activities. The framework was evaluated through a case study based on data collected in a real environment. Results point to the feasibility of the proposal.