A multimodal approach for human activity recognition based on skeleton and RGB data

Human action recognition plays a fundamental role in the design of smart solution for home environments, particularly in relation to ambient assisted living applications, where the support of an automated system could improve the quality of life of humans trying to interpret and anticipate user needs, recognizing unusual behaviors or preventing dangerous situations (e.g. falls). In this work the potentialities of the Kinect sensor are fully exploited to design a robust approach for activity recognition combining the analysis of skeleton and RGB data streams. The skeleton representation is designed to capture the most representative body postures, while the temporal evolution of actions is better highlighted by the representation obtained from RGB images. The experimental results confirm that the combination of these two data sources allow to capture highly discriminative features resulting in an approach able to achieve state-of-the-art performance on public benchmarks.