Performance and ergonomics of automated versus manual validation for AR-supervised industrial operations

Augmented Reality has proven to be a viable solution compared to paper instructions for the supervision of maintenance procedures in industrial contexts to reduce error rates and increase overall performance. With the use of computer vision and cyber physical systems, the validation of specific tasks in AR-supervised environments can be automated to increase process quality even more. However, in the context of human-centered approaches such as industry 5.0, the impact of such methods on operator’s acceptability has yet to be estimated. Therefore, the paper presents a quantitative study on 24 participants to compare 4 modalities of validation of operational procedures. SCRAM, a web application based on WebXR, is developed for the supervision of maintenance operations to compare 2 types of devices: tablets and AR headsets. All scenarios are compared on an assembly use case based on performance criteria and subjective questionnaires such as NASATLX, System Usability Scale (SUS) and Technology Acceptance Model to measure acceptability. In the end, results mostly show significant differences in completion times, usability, and perceived ease of use of the SCRAM application regarding the choice of the device. On the other hand, the study does not highlight any significant difference between scenarios with automated validation of operations and scenarios with manual validation.