  This paper presents a new method to describe spatio-temporal relations
between objects and hands, to recognize both interactions and activities within
video demonstrations of manual tasks. The approach exploits Scene Graphs to
extract key interaction features from image sequences while simultaneously
encoding motion patterns and context. Additionally, the method introduces
event-based automatic video segmentation and clustering, which allow for the
grouping of similar events and detect if a monitored activity is executed
correctly. The effectiveness of the approach was demonstrated in two
multi-subject experiments, showing the ability to recognize and cluster
hand-object and object-object interactions without prior knowledge of the
activity, as well as matching the same activity performed by different
subjects.
