Activity recognition is a key component of patient management in smart homes where high-level activities can be learned from low-level sensor data. Different activities have different durations. In addition different people may take different amounts of time to complete the same activity. Activity duration information can therefore be considered as a potentially useful feature in assessing user health and cognitive status, and in distinguishing between different activities. The objective of this thesis is to develop methods that incorporate duration-based information in activity recognition and thus improve activity prediction performance. Activity duration information has been integrated in an existing probabilistic model and improvements in activity recognition analysed. For the purpose of computational modelling, duration data were discretised. A probabilistic learning model was built using the joint probability distribution over different activities, representing behavioural patterns of the users in performing a range of activities. Each activity was predicted based on the conditional probability of the activity given the sequence of sensor activations, the time of activation and the duration of the activity. The built model demonstrated nearly 2% improvement in the prediction of activities when duration information was included. The derived model with enhanced recognition capability motivated the development of a duration-based decision making framework for a potential online support tool. The aim was to combine two incomplete aspects of online sensor data: incomplete activity duration and partially observed sensor activations within such a framework. The two aspects, when integrated can improve the online prediction of user activity. As an activity progresses, these two aspects change over time; hence the prediction of the current activity will also change accordingly. Further work related to activity durations involved exploring different clustering approaches for the purpose of discretisation of duration data related to a set of activities and automation of the discretisation process. The work also addressed issues associated with the discretisation problem when working with a dataset of limited size, where prediction of the statistical model parameters is difficult. In summary, the research presented in this thesis contributes to methodologies to enhance activity recognition systems for smart homes based on the incorporation of activity duration information. The advantage of employing activity duration data in activity recognition was also demonstrated on datasets from external smart home environments, where different activities were distinguished based on durations along with other sensor attributes.
|Electronic Thesis or Dissertation
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