The Other Kind of Machine Learning: Modeling Worker State for Optimal Training of Novices in Complex Industrial Processes

Abstract

In the context of Industry 4.0, there is a strong focus on man-machine interaction, and a push for ICT solutions in industrial applications. One aspect of this are industrial assistance systems, both to aid operators in their work and to train novice workers in complex processes. Addressing the latter purpose, in this work, a training station e-learning concept is detailed, with the purpose to automatically teach a novice worker the necessary steps to assemble an alpine ski without the need for constant human supervision. It is designed to observe and especially model the state of the trainee for optimal support via delivery of instructional material and feedback based on an evaluation of the trainee's needs and behavior. The training station is comprised of a work bench, displays to deliver instructional material, and various sensors to monitor both the trainee's progress and overall state. To enable best possible worker support, a model of worker state (Idle, Flow, Busy, Overload) is proposed which is derived from analysis of the sensor data. It enables the system to provide dynamic assistance in which feedback is fine-tuned to meet the trainee's needs and deliver information precisely, and only when it is needed.