Monitoring human activities using wearable wireless sensor nodes has the potential to enable many useful applications for everyday situations. The deployment of a compact and computationally efficient grammatical representation of actions reduces the complexities involved in the detection and recognition of human behaviors in a distributed system. In this paper, we introduce a road map to a linguistic framework for the symbolic representation of inertial information for physical movement monitoring. Our method for creating phonetic descriptions consists of constructing primitives across the network and assigning certain primitives to each movement. Our technique exploits the notion of a decision tree to identify atomic actions corresponding to every given movement. We pose an optimization problem for the fast identification of primitives. We then prove that this problem is NP-Complete and provide a fast greedy algorithm to approximate the solution. Finally, we demonstrate the effectiveness of our phonetic model on data collected from three subjects.