Towards Skill Recognition using Eye-Hand Coordination in Industrial Production

Companies are re-focusing and making use of human labor in order to create individualized lot-size-1 products and not produce the exact same mass product again and again. While human workers can produce with a least with the same quality as machines do, they are not that consistent, so it's better to combine strengths of both men and machines. In this work, we investigate how we can utilize how humans behave in relation to task-required skills levels. To do so we investigate hand-eye coordination onprecision tasks, its relation to fine and gross motor skills, in an unconstrained industrial setting. This setting consists of an up to 22 tasks assembly processes of two variants of a high quality product. We establish that there is a high correlation between expected task required skill level and the captured hand eye coordination of expert factory workers and that hand eye coordination can be used to distinguish between fine and gross motor skills. In addition we provide insights how this can be exploited in future work.