Deep learning for automatic usability evaluations based on images: A case study of the usability heuristics of thermostats

Deep learning for automatic usability evaluations based on images: A case study of the usability... Thermostats are designed for increasing requirements on indoor thermal comfort. Nevertheless, they are critical devices for saving energy in buildings and households. However, when thermostats do not accomplish the usability requirements, the end-users do not save energy. Then, when a thermostat is designed or validated, one of the leading problems that must be tackled is the usability evaluation. Generally, the evaluation is based on usability heuristics that are done by experts and designers and involve a very complicated cycling process in which usability experts need to be included in the complete usability evaluation. On the other hand, there are several proposals for generating an automatic usability analysis that can be used by designers or end-users. However, they are limited by the methodologies that are implemented in the evaluation because usability evaluations necessitate a large amount of data abstraction, and the amount of processed information is enormous; As an alternative, Artificial Intelligence can help to solve this problem, especially machine learning techniques with deep learning capabilities that can reach a high level of data abstraction with a significant amount of information and implement an automatic usability evaluation based on images. Convolutional networks that are included in deep learning can classify complex problems, attain highly accurate results. This paper proposes to train a convolutional network with standard usability heuristics for evaluating usability, which is an easy method for evaluating usability in thermostats, based on images. The proposed automatic method gives excellent results for evaluating usability heuristics in the heuristic assigned. This paper provides a complete methodology, using deep learning, for automatically evaluating the usability heuristics of thermostats. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Energy and Buildings Elsevier

Deep learning for automatic usability evaluations based on images: A case study of the usability heuristics of thermostats

Loading next page...
 
/lp/elsevier/deep-learning-for-automatic-usability-evaluations-based-on-images-a-BELPNxJd0Q
Publisher
Elsevier
Copyright
Copyright © 2017 Elsevier B.V.
ISSN
0378-7788
eISSN
1872-6178
D.O.I.
10.1016/j.enbuild.2017.12.043
Publisher site
See Article on Publisher Site

Abstract

Thermostats are designed for increasing requirements on indoor thermal comfort. Nevertheless, they are critical devices for saving energy in buildings and households. However, when thermostats do not accomplish the usability requirements, the end-users do not save energy. Then, when a thermostat is designed or validated, one of the leading problems that must be tackled is the usability evaluation. Generally, the evaluation is based on usability heuristics that are done by experts and designers and involve a very complicated cycling process in which usability experts need to be included in the complete usability evaluation. On the other hand, there are several proposals for generating an automatic usability analysis that can be used by designers or end-users. However, they are limited by the methodologies that are implemented in the evaluation because usability evaluations necessitate a large amount of data abstraction, and the amount of processed information is enormous; As an alternative, Artificial Intelligence can help to solve this problem, especially machine learning techniques with deep learning capabilities that can reach a high level of data abstraction with a significant amount of information and implement an automatic usability evaluation based on images. Convolutional networks that are included in deep learning can classify complex problems, attain highly accurate results. This paper proposes to train a convolutional network with standard usability heuristics for evaluating usability, which is an easy method for evaluating usability in thermostats, based on images. The proposed automatic method gives excellent results for evaluating usability heuristics in the heuristic assigned. This paper provides a complete methodology, using deep learning, for automatically evaluating the usability heuristics of thermostats.

Journal

Energy and BuildingsElsevier

Published: Mar 15, 2018

References

You’re reading a free preview. Subscribe to read the entire article.


DeepDyve is your
personal research library

It’s your single place to instantly
discover and read the research
that matters to you.

Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.

All for just $49/month

Explore the DeepDyve Library

Search

Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly

Organize

Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.

Access

Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.

Your journals are on DeepDyve

Read from thousands of the leading scholarly journals from SpringerNature, Elsevier, Wiley-Blackwell, Oxford University Press and more.

All the latest content is available, no embargo periods.

See the journals in your area

DeepDyve

Freelancer

DeepDyve

Pro

Price

FREE

$49/month
$360/year

Save searches from
Google Scholar,
PubMed

Create lists to
organize your research

Export lists, citations

Read DeepDyve articles

Abstract access only

Unlimited access to over
18 million full-text articles

Print

20 pages / month

PDF Discount

20% off