Analysis of the Everyday Human Environment Via Large Scale Commonsense Reasoning

In this work, we present a system, SRCS, which is designed to model and reason about the state of the everyday human environment using large scale commonsense reasoning. SRCS makes use of lightweight wearable sensors to observe human activity, and incorporates statistical learning techniques and preexisting sources of commonsense data to produce a graphical model of the state of the human environment over time. To improve the effectiveness of our system, we use semi-supervised machine learning techniques to learn a model based on sparsely labeled training data. Finally, to improve the efficiency of this model, we introduce a means of inferring a context - i.e. a subset of particularly relevant information - to the observations the system may encounter, and use this context discovery to produce more efficient reasoning. We present experimental results that confirm the efficacy of our techniques, and conclude with current directions of our research and future work.