Congeston recognition for arm navigation

We have built an arm-navigation assisting system for a visually impaired person (user) to reach an object on the table, where optical tracking of marks attacked both on the objects and on his arm is used in order to augment his sight. The system helps him with giving spacial information of the workspace so that he creates a cognitive map of the workspace. For this purpose degrees of congestion on the workspace must be conveyed to the user. Starting from the description of the assisting system, we propose in this paper a method of judging the degrees of congestion of the workspace around arm. There are five of them: from “narrow” to “broad,” which are determined by using well-established Neural Network techniques on the basis of the spacial data obtained by the Distance Field Model (DFM) representation of the workspace. Defining spaciousness by entropy-like measure based on the DFM data is also proposed separately.