Event-driven contexts in manufacturing occur pervasively as a result of interactions among
involved entities such as machines, workers, materials, and environment. One of the primary
tasks in smart manufacturing is to derive a context-aware system conveniently incorporating
worker knowledge for generating timely actionable intelligence for workers on factory floor
and supervisors to respond. In this paper, we propose to design a human-and-machine
interaction recognition framework by using a causality concept to collect contextual data for …