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Identifying on-going activities for the provision of services that are capable of matching the needs of users poses a number of daunting challenges. Most existing approaches to activity recognition require training offline activity models before being applied to the identification of activities in real time. However, the dynamic nature of actual living environments can make previously learned activity models irrelevant. This study addressed the problem of learning and recognizing daily activities in a dynamic smart-home environment, using a novel approach referred to as hybrid user-assisted incremental model adaptation. This approach involves reconfiguring previously learned activity models within a dynamic environment, while pursuing maximum efficiency by using assistance from users as well as the system to annotate new training data. Experiments that are conducted in a fully equipped smart-home lab demonstrate the efficacy of the proposed approach.