This talk introduces a theoretical framework based on approximate reasoning, it first extends Euclidean transformations into quantity space via the proposed fuzzy qualitative algebra, next, system behavior is represented by a set of automatically generated sampling data, then, data analysis methods are selected to extract features of the dataset according to individual application context, finally system behavior and corresponding data features are integrated at the system level. The framework is presented in terms of robotics, it has been adapted into applications with encouraging results such as hand gesture recognition, human motion analysis. A unified approach to human/prosthetic hand gesture recognition and results in vision/capture based human motion analysis will be reported in the talk. The framework is intended to provide a foundation towards a unified representation by “gluing” hybrid representations.