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This work presents the unification and formal analysis of occurring Activities of Daily Living (ADLs) identified by an intelligent well-being monitoring system used for elderly residents in extra care homes. The ADLs considered in this paper are: i) personal grooming and toilet, ii) preparation of breakfast, iii) preparation of lunch, iv) preparation of evening meal and v) sleep. These ADLs are examined as they exhibit multiple or similar occurrences during a typical day. The novelty of this work lies in the introduction of a unification approach that could help for the detection of normal and abnormal behaviour based on the execution of the ADLs from elders in extra care homes equipped with different types of sensors. To unify and detect these types of behaviour, temporal aspects of the ADLs' execution like their duration and time of day are scrutinised. Moreover, the formal analysis of the identified ADLs is conducted, using Petri Nets for the modelling of these activities and model checking for their verification. Finally, the verification results are used to indicate whether an abnormal behaviour takes places during an activity, which could be used as a measure for spotting potential health issues regarding the elders that reside in the monitored homes.

The daily monitoring of ageing population is a current issue which can be effectively tackled by applying daily activity monitoring via smart sensing technology. The purpose of the monitoring is mostly aimed at collecting health conditional related activity awareness and emergency events detection. This is a pilot study that uses low pixel resolution infrared sensors for nonintrusive human activity detection and recognition without body attachments and taking of individual image. In this work, we design and implement a multiple IR sensors system and a serial experiment to verify the availability of applying low-resolution IR data for human activity recognition for both single and multiple target scenarios in the healthcare context. In the experimental setup, the sensor system achieves 82.44% accuracy in general and reaches 100% accuracy rate for some particular activities. The work proves that the low-resolution IR information is an effective metric for human activity monitoring in healthcare applications. Infrared sensors. Human activity detection. Classification methods. Elderly care homes.

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