Skip to Main Content
Multi-resident activity recognition is among a key enabler in many context-aware applications in a smart home. However, most of prior researches ignore the potential interactions among residents in order to simplify problem complexity. On the other hand, multiple-resident activities are usually recognized using cameras or wearable sensors. However, due to human-centric concerns, it is more preferable to avoid using obtrusive sensors. In this paper, we propose dynamic Bayesian networks which extend coupled hidden Markov models (CHMMs) by adding some vertices to model both individual and cooperative activities. In order to improve performance of the model, we categorize sensor observations based on data association and some domain knowledge to model multiple-resident activity patterns. We then validate the performance using a multi-resident dataset from WSU (Washington State University), which only includes non-obtrusive sensors. The experimental result shows that our model performs better than other baseline classifiers.