• learning enhancement
• man
• technological solutions

The research project aimed at developing and validating an Augmented Reality demonstrator to test the feasibility of introducing the technology at Niftylift and to enhance its training process, improving learning time and error rate performances. The project was carried out in collaboration with Niftylift, a worldwide supplier of cherry pickers, at its Milton Keynes site, UK.
The applied methodology was the following. First, the project was defined in collaboration with the Client. Then, the best practices provided by literature were combined with observations and interviews to define the tool’s requirements. Software and hardware were selected accordingly, and the tool was developed. The last phase involved tool validation and the project was concluded with quantitative and qualitative benefits assessment.
The application environment selected was the existing training bay. The tool focused on covering the assembly of hydraulic hoses to the relative valve; the choice was driven by Company needs. Requirements lead to the choice of Microsoft HoloLens as hardware, while Unity and Vuforia were used as software.
The tool provides sequential instructions through texts, images and animations. Images and markers were used to recognise elements and to overlay associated information on them. Users can choose whether to resume instructions or to see them several times. While the process goes on and similar operations are repeated, the number of instructions provided at first are reduced, to enhance the learning process. Users can still access complete instructions under request.
Benefits were assessed comparing time and error rate performances among two groups: the first performed the process following SOPs, while the second group used the AR tool. Groups were defined such that the influence of other variables on the average performances was avoided as much as possible.
Results showed improvements when introducing AR for error rates performances and for the average assembly times. It was concluded that AR can be a feasible and valid solution to address Niftylift needs.