Performance and ergonomics of automated versus manual validation for AR-supervised industrial operations
Résumé
Validation tasks in AR-supervised environments can be automated to improve process quality in industrial contexts. However, human-centered approaches urge us to study the impact of such methods on operator's acceptance. The paper presents a study on 24 participants to compare 4 modalities of validation of operations. SCRAM, a WebXR application is developed to compare validation tasks on 2 types of devices: tablets and HMDs. All scenarios are tested with an assembly use case through performance, task load, usability and acceptance criteria. Result show significant differences regarding the choice of the device but do not highlight any difference between scenarios with automated validation of operations and scenarios with manual validation.