Identifying human intention during assembly operations using wearable motion capturing systems including eye focus

Abstract

Simulating human motion behavior in assembly operations helps to create efficient collaboration plans for humans and robots. However, identifying human intention may require high quality human motion capture data in order to discriminate micro-actions and human attention. In this regard, a human motion capture setup that combines various systems such as joint body, finger, and eye trackers is proposed in combination with a methodology of identifying the intention of human operators as well as for predicting sequences of activities. The approach may lead to safer human-robot collaboration.