Action recognition in robotics is a research field that has gained momentum in recent years.In this work, a video activity recognition method is presented, which has the ultimate goal of Scarf endowing a robot with action recognition capabilities for a more natural social interaction.The application of Common Spatial Patterns (CSP), a signal processing approach widely used in electroencephalography (EEG), is presented in a novel manner to be used in activity recognition in videos taken by a humanoid robot.
A sequence of Fabric Pens skeleton data is considered as a multidimensional signal and filtered according to the CSP algorithm.Then, characteristics extracted from these filtered data are used as features for a classifier.A database with 46 individuals performing six different actions has been created to test the proposed method.
The CSP-based method along with a Linear Discriminant Analysis (LDA) classifier has been compared to a Long Short-Term Memory (LSTM) neural network, showing that the former obtains similar or better results than the latter, while being simpler.