Developing an Augmented Reality Tourism App through User-Centred Design (Extended Version)
January 29, 2020 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Meredydd Williams, Kelvin K. K. Yao, Jason R. C. Nurse
arXiv ID
2001.11131
Category
cs.HC: Human-Computer Interaction
Cross-listed
cs.CY,
cs.GR,
cs.SE
Citations
6
Venue
arXiv.org
Last Checked
4 months ago
Abstract
Augmented Reality (AR) bridges the gap between the physical and virtual world. Through overlaying graphics on natural environments, users can immerse themselves in a tailored environment. This offers great benefits to mobile tourism, where points of interest (POIs) can be annotated on a smartphone screen. While a variety of apps currently exist, usability issues can discourage users from embracing AR. Interfaces can become cluttered with icons, with POI occlusion posing further challenges. In this paper, we use user-centred design (UCD) to develop an AR tourism app. We solicit requirements through a synthesis of domain analysis, tourist observation and semi-structured interviews. Whereas previous user-centred work has designed mock-ups, we iteratively develop a full Android app. This includes overhead maps and route navigation, in addition to a detailed AR browser. The final product is evaluated by 20 users, who participate in a tourism task in a UK city. Users regard the system as usable and intuitive, and suggest the addition of further customisation. We finish by critically analysing the challenges of a user-centred methodology.
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