Categorisation of future applications for Augmented Reality in human lunar exploration
November 19, 2022 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Paul Topf Aguiar de Medeiros, Paul Njayou, Flavie Rometsch, Tommy Nilsson, Leonie Becker, Aidan Cowley
arXiv ID
2301.00838
Category
cs.HC: Human-Computer Interaction
Citations
2
Venue
arXiv.org
Last Checked
4 months ago
Abstract
The European Space Agency (ESA) has a clear mission to go forward to the Moon in preparation of human presence on Mars. One of the technologies looked at to increase safety and efficiency of astronauts in this context is Augmented Reality (AR). This technology allows digital visual information to be overlaid onto the user's environment through some type of display or projector. In recent years separate studies have been conducted to test the potential value of AR for astronauts by implementing a few functionalities on an AR display followed by testing in terrestrial analogue environments. One of the groups contributing to these investigations is Spaceship EAC (SSEAC). SSEAC is a group of interns and trainees at the European Astronaut Centre (EAC) focusing on emerging technologies for human space exploration. This paper presents an outcome of SSEAC's activities related to AR for lunar extravehicular activities (EVAs), in which an approach similar to design thinking was used to explore, identify, and structure the opportunities offered by this technology. The resulting categorization of AR use cases can be used to identify new functionalities to test through prototyping and usability tests and can also be used to relate individual studies to each other to gain insight into the overall potential value AR has to offer to human lunar exploration. The approach adopted in this paper is based on the Fuzzy Front End (FFE) model from the innovation management domain. Utilising a user-driven instead of technology-driven method resulted in findings that are relevant irrespective of the hardware and software implementation. Instead, the outcome is an overview of use cases in which some type of AR system could provide value by contributing to increased astronaut safety, efficiency and/or efficacy.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Human-Computer Interaction
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Improving fairness in machine learning systems: What do industry practitioners need?
R.I.P.
π»
Ghosted
Identifying Stable Patterns over Time for Emotion Recognition from EEG
R.I.P.
π»
Ghosted
Questioning the AI: Informing Design Practices for Explainable AI User Experiences
R.I.P.
π»
Ghosted
Deep Learning for Sensor-based Human Activity Recognition: Overview, Challenges and Opportunities
R.I.P.
π»
Ghosted
Educational data mining and learning analytics: An updated survey
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted