A Distributed Hidden Markov Model for Fine-grained Annotation in Body Sensor Networks

Human movement models often divide movements into parts. In walking the stride can be segmented into four different parts, and in golf and other sports, the swing is divided into section based on the primary direction of motion. When analyzing a movement, it is important to correctly locate the key events dividing portions. There exist methods for dividing certain actions using data from speci¿c sensors. We introduce a generalized method for event annotation based on Hidden Markov Models. Genetic algorithms are used for feature selection and model parameterization. Further, collaborative techniques are explored. We validate this method on a walking dataset using inertial sensors placed on various locations on a human body. Our technique is computationally simple to allow it to run on resource constrained sensor nodes.