In this paper we present a method for determining body orientation and pose information from laser scanner data using particle filtering with an adaptive modeling algorithm. A parametric human shape model is recursively updated to fit observed data after each resampling step of the particle filter. This updated model is then used in the likelihood estimation step for the following iteration. This method has been implemented and tested by using a network of laser range finders to observe human subjects in a variety of interactions. We present results illustrating that our method can closely track torso and arm movements even with noisy and incomplete sensor data, and we show examples of body language primitives that can be observed from this orientation and positioning information.