A Context-Aware Assistance Framework for Implicit Interaction with an Augmented Human

The automotive industry is currently facing massive challenges. Shorter product life cycles together with mass customization lead to a high complexity for manual assembly tasks. This induces the need for effective manual assembly assistances which guide the worker faultlessly through different assembly steps while simultaneously decrease their completion time and cognitive load. While in the literature a simulation-based assistance visualizing an augmented digital human was proposed, it lacks the ability to incorporate knowledge about the context of an assembly scenario through arbitrary sensor data. Within this paper, a general framework for the modular acquisition, interpretation and management of context is presented. Furthermore, a novel context-aware assistance application in augmented reality is introduced which enhances the previously proposed simulation-based assistance method by several context-aware features. Finally, a preliminary study (N = 6) is conducted to give a first insight into the effectiveness of context-awareness for the simulation-based assistance with respect to subjective perception criteria. The results suggest that the user experience is improved by context-awareness in general and the developed context-aware features were overall perceived as useful in terms of error, time and cognitive load reduction as well as motivational increase. However, the developed software architecture offers potential for improvement and future research considering performance parameters is mandatory.