Assisted Ambient Living (AAL) focuses on self-sufficiency, assisting disabled people to perform activities of daily living (ADL) by automating assistive actions in smart environments. Importantly, AAL provides opportunities for dynamically guiding patients with a cognitive decline through an ADL. Activity recognition is a pivotal task since it allows detecting when an ADL is started by recognizing its constituent activities. When dealing cognitive decline, activity recognition should also be able to detect when activities are performed incorrectly–e.g., performed out-of-order, at the wrong location or time, or with the wrong objects (e.g., utensils) – which is nevertheless not a common goal in activity recognition. Moreover, it should be able to cope with non-uniform ways of performing the ADL that are nevertheless correct. We present a novel knowledge-driven activity recognition approach, which employs semantic reasoning to recognize both correct and incorrect actions, based on the ADL workflow as well as associated environment context.
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