The ability to assess the neurological state of patients with neurodegenerative diseases on a continuous basis is an important component of future care for these chronically ill patients. In this paper we describe a set of algorithms to infer gait velocity and its variability using data from an unobtrusive sensor network by incorporating a simple dynamic description of a patient's movements within his or her residence. The sensors include a combination of passive motion detectors and active radio frequency identification tags. The dynamic model is a simple 4 state hidden Markov model. We investigated the ability of this model to assess gait velocity and its variability using data from a six month pilot study of several patients with early stage Parkinson's disease.