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Human Activity Recognition (AR) is an area of great importance for health and well-being applications including Ambient Intelligent (AmI) spaces, Ambient Assisted Living (AAL) environments, and wearable healthcare systems. Such intelligent systems reason over large amounts of sensor-derived data in order to recognise users’ actions. The design of AR algorithms relies on ground-truth data of sufficient quality and quantity to enable rigorous training and validation. Ground-truth is often acquired using video recordings which can produce detailed results given the appropriate labels. However, video annotation is not a trivial task and is, by definition, subjective. In addition, the sensitive nature of the recordings has to be foremost in minds of the researchers to protect the identity and privacy of participants. In this paper, a hierarchical ontology for the annotation of human activity recognition in the home is proposed. Strategies that support different levels of granularity are presented enabling consistent, and repeatable annotations for training and validating activity recognition algorithms. Best practice regarding the handling of this type of sensitive data is discussed.

A Human Activity Recognition Framework for Healthcare Applications: Ontology, Labelling Strategies, and Best Practice