Activity, context, and plan recognition with computational causal behaviour models
As computers are becoming more and more a part of our everyday life, the vision of Mark Weiser about ubiquitous computing becomes true. One of the core tasks of such devices is to assist the users in achieving their goals. To do this, the assistive system has to have knowledge about the current situation as well as the user's goal. Such knowledge allows the assistive system to provide strategies to support the users in achieving their goals beginning from the current situation. A GPS navigation device is a simple, yet well known instance of such an assistive system. It recommends a route based on the current location and the manually specified goal. Obviously, effective assistance can only be provided if accurate knowledge about the user's situation and his goal is available. This requires to reason about the actions of the user and to cope with uncertainties that are inherent to human behaviour. The problem becomes even harder, as in real world settings, users cannot be observed directly but through sensors that introduce noise and ambiguity as additional sources of uncertainty. Several applications in the literature showed that probabilistic methods can be used to infer the required information from sensor data. However, massive amounts of training data are needed in order to train classifiers to achieve good recognition rates. This is expensive and prevents trained models from being reused. Recently, researchers employed models of human behaviour in order to reduce the need for training data. These models are generalisable they allow the specification of human behaviour without the need for training samples. To this end, these models can be reused in different settings. While these models allow the synthesis of probabilistic models, only few attempts have been made to assess their capabilities with respect to low level sensors such as accelerometers. In fact, different researchers stated that inferring high level knowledge about the user from low level sensor data is an open research topic. To address the above problems, objective of this thesis is to answer the question "how to achieve efficient sensor based reconstruction of causal structures of human behaviour in order to provide assistance?" To achieve that, in the first step the meaning of this question is analysed and requirements for an inference system are derived. A review of the literature is then conducted and a meta analysis is performed to assess the capabilities of the different approaches and the complexity of their evaluation setting. The results of this analysis show that none of the approaches from the literature satisfies all requirements. To answer the research question, the concept of Computational Causal Behaviour Models (CCBMs) is introduced. CCBM allows the specification of human behaviour by means of preconditions and effects and employs Bayesian filtering techniques to reconstruct action sequences from noisy and ambiguous sensor data. Furthermore, a novel approximative inference algorithm the Marginal Filter is introduced. The Marginal Filter is specifically tailored for categorical state spaces, which are generated by CCBM. To investigate the capabilities with respect to recognition performance and reusability, different experiments are then conducted. Each experiment addresses different aspects of the research question. A detailed analysis of the results of these experiments shows that CCBM is able to achieve good recognition rates. Moreover, the Marginal Filter is shown to outperform the standard method for approximative Bayesian inference the Particle Filter. Furthermore, it is shown that CCBM satisfies the requirements.