Preparation of Human-Testing Experiment to Determine Key Features for Augmented Reality Applications to Succeed

13 pages•Published: November 24, 2022

Abstract

The evolution of mobile technology has placed augmented reality (AR) into the hands of previously inaccessible users. AR, which previously required specialized hardware devices, is now capable of running on most smartphones and tablets. Due to the abundance of AR capable devices, the medium is being explored by developers to create novel applications for various purposes, such as entertainment and education. With an ever-growing supply of AR applications, only a minority ever flourish. Through a thorough investigation of fields currently using AR, this paper hypothesizes the following are key features to a successful AR application: safety in the real world, visualization of information, affordances of virtual objects, and the use of a real-world environment. To design quality experiments proficient in evaluating effectiveness of the hypothesized features, a deep dive into exemplary experiment design and subsequent pitfalls was conducted. With this information, this paper presents the methodology of the proposed experiments that yield quantitative feedback for each feature, as well as safety and privacy forms for participants. Once conducted, these experiments will yield results that may impact the future of AR application development.

Keyphrases: ar feature testing, augmented reality, user study

In: Frederick Harris, Alex Redei and Rui Wu (editors). Proceedings of 31st International Conference on Software Engineering and Data Engineering, vol 88, pages 48-60.

Links:	https://easychair.org/publications/paper/CNRV
	https://doi.org/10.29007/v7bp

BibTeX entry

@inproceedings{SEDE2022:Preparation_Human_Testing_Experiment,
  author    = {Nicholas Henning and Bradford Towle},
  title     = {Preparation of Human-Testing Experiment to Determine Key Features for Augmented Reality Applications to Succeed},
  booktitle = {Proceedings of 31st International Conference on Software Engineering and Data Engineering},
  editor    = {Frederick Harris and Alex Redei and Rui Wu},
  series    = {EPiC Series in Computing},
  volume    = {88},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {/publications/paper/CNRV},
  doi       = {10.29007/v7bp},
  pages     = {48-60},
  year      = {2022}}

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