Augmented reality (AR) technologies have been introduced in manufacturing planning functions. Working in augmented environments, users usually select virtual objects with hand gestures that are associated with arm fatigue. In this paper, a Steady-State Visual Evoked Potentials (SSVEP) Brain-Computer Interface (BCI) for ‘hologram’ selection in AR is proposed. The BCI and hand gesture performances, workload, and usability have been measured for 4 different tasks. Although the results showed that BCI were on average 2.52 seconds slower than hand gestures, the BCI was more precise than hand gestures, with accuracies close to 100%. The BCI had an overall workload of 38.52, that resulted to be lower than the hand gestures’ one of 52.40. Finally, the BCI’s System Usability Scale of 77.8 overcame the hand gestures one of 11.3 points. These results highlighted the potential of coupling together BCI and AR, demonstrating a possible future application of these technologies in industrial settings.

Human performance and mental workload in augmented reality: brain computer interface advantages over gestures / DA COL, Silvio; Kim, Eunsik; Sanna, Andrea. - In: BRAIN COMPUTER INTERFACES. - ISSN 2326-263X. - ELETTRONICO. - (In corso di stampa). [10.1080/2326263X.2022.2068324]

Human performance and mental workload in augmented reality: brain computer interface advantages over gestures

Silvio Da Col;Andrea Sanna
In corso di stampa

Abstract

Augmented reality (AR) technologies have been introduced in manufacturing planning functions. Working in augmented environments, users usually select virtual objects with hand gestures that are associated with arm fatigue. In this paper, a Steady-State Visual Evoked Potentials (SSVEP) Brain-Computer Interface (BCI) for ‘hologram’ selection in AR is proposed. The BCI and hand gesture performances, workload, and usability have been measured for 4 different tasks. Although the results showed that BCI were on average 2.52 seconds slower than hand gestures, the BCI was more precise than hand gestures, with accuracies close to 100%. The BCI had an overall workload of 38.52, that resulted to be lower than the hand gestures’ one of 52.40. Finally, the BCI’s System Usability Scale of 77.8 overcame the hand gestures one of 11.3 points. These results highlighted the potential of coupling together BCI and AR, demonstrating a possible future application of these technologies in industrial settings.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11583/2961863