Neural networks for recognizing human activities in home-like environments

Using Context-Awareness information in human-robot shared environments enhances the characterization of the interaction scenario, improving the experience of human collaboration with the robot. Aspects such as environmental location and human-robot dialog have traditionally been used to infer the current situation, which can be helpful in the decision making process of any robot. However, the acoustic signals available in every interaction scenario are commonly obviated, thus removing an important source of information. This paper presents the design, development and tests of a Context Awareness Component that labels users' activities using localization information, dialog flow, and time of day, and adds an environment recognition component supported by acoustic signals that improves the inference system. To this end, this research proposes a feedforward neural network solution based on a multilayer perceptron approach. The paper also discusses the configuration of the neural network for optimizing the recognition of human activity in "at home" environments, using the four inputs previously mentioned. Finally, the validation of the approach proposed is done by comparing the results when the sound recognition system is used and when it is not.