Using conditional random fields to validate observations in a 4W1H paradigm

Detecting human activity has been one of the main focuses in intelligent spaces. This is achieved by using a large number of sensors attached both to the humans and the environment. Yet, these systems are prone to failure due to the parallel sensing when miss firings occur. We propose a method to test and prevent the miss firings using conditional random fields, since they provide us with a tool that allows us to confirm whether the expected output or activity is likely to happen in the space or not, given the inputs of the system, which are provided by the 4W1H paradigm, that allows us to segment every piece of information in the space into 5 simple variables (Who, When, What, Where and How).