The problem of occupant activity recognition has gained in relevance due to demographic shifts and growing environmental concerns where context-sensitive applications promise to help. The prevalent approach to this problem is based around the use of supervised machine learning, which faces challenges due to its requirement for large amounts of annotated training data and its tendency to overfit. Using preexisting common sense or expert knowledge, usually in the form of ontologies, presents another option, but carries its own set of shortcomings. Recently, the usage of hierarchical task network planning as an alternative to this ontological approach has been proposed. Hybrid systems that utilize both machine learning and preexisting knowledge promise to preserve the strength of both approaches while alleviating their drawbacks. We propose a new hybrid occupant activity system using hierarchical task network planning to support the training of a Hidden Markov Model, which, to the best of our knowledge, has not been done before. In addition, we evaluate the system on real sensor data in order to find out how much merits this new design has. Hereby we attempt and compare multiple approaches to the problem. Although not all methods improve the performance, the results show that the basic idea is sound and can generate measurable improvements.