A Language Approach to Modeling Human Behaviors

The modeling of human behavior becomes more and more important due to the increasing popularity of context-aware computing and people-centric applications. Inspired by the principle of action-as-language, we propose that human ambulation behavior share similar properties as natural languages. In this paper, we use a Life Logger system to build the behavior language corpus. The behavior corpus shows Zipf's distribution over the frequency of vocabularies which is aligned with our "Behavior as Language" assumption. Our preliminary results of using smoothed n-gram language model for activity recognition achieved an average accuracy rate of 94% in distinguishing among basic behaviors including walking, running, and cycling. This behavior-as-language corpus will enable researchers to study higher level human behavior based on the syntactic and semantic analysis of the data.