| Activity recognition (AR) is a key technology for developing human-centric applications in smart environments. However, state-of-the-art AR technology cannot be used for addressing real world problems due to insufficient accuracy and lack of a usable activity recognition programming model. To address these issues, a new AR approach is developed in this dissertation.;AR performance is strongly dependent on the accuracy of the underlying activity model. Therefore, it is essential to examine and develop an activity model that can capture and represent the complex nature of human activities more precisely. To address this issue, we introduce generic activity framework (GAF) and activity semantics. The GAF is a refined hierarchical composition structure of the traditional activity theory. Activity semantics are highly evidential knowledge that can identify activities more accurately in ambiguous situations. We compare our activity model with traditional activity model in terms of attainable recognition certainty.;Two new AR algorithms---Multilayer Neural Network (MLNNK) based algorithm and fuzzy logic (FL) based algorithm---have been developed in this dissertation. These algorithms utilize and work in tandem with the developed activity framework and model. The MLNNK based AR algorithm illustrates the high recognition accuracy of generic activity framework modeling approach. FL based AR algorithm utilizes both activity semantics and generic activity framework.;For achieving high accuracy, it is important to identify and mitigate the most debilitating sources of uncertainty. The efficacy of AR systems is usually quantified based on the recognition accuracy at the final step of activity recognition process. This method does not reveal the uncertainty sources that affect overall performance significantly. Therefore, it is necessary to quantify every possible uncertainty source through all activity recognition procedures. To address this issue, metrics and measurement methods for each uncertainty source are developed.;AR technology should provide programmable interface to developers to support AR system design change according to new application requirements or AR environment changes. This dissertation classifies developers into three categories: smart space developer, activity model and algorithm developers, and application developers. The hierarchical aspects of our generic activity framework decouple the observation subsystem from the rest of the activity model. We demonstrate the value of this decoupling by experimentally comparing the level of effort needed in making sensor changes. |
